How Image Sensors Impact Video Forensics

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How Image Sensors Impact Video Forensics

Video Forensics

The type, quality, and technology behind digital camera image sensors plays a major role in video forensics and how digital images and video enhancements are completed.

The technology of video forensics is impacted greatly by the technology used in recording a video, or capturing still images. The image sensor is a main component in all digital cameras. Knowledge of the technology of Image sensors and image scanning techniques is essential in the field of video forensics. An Image sensor consists of many photo sites which correspond to the pixels in an image. Each photo site registers the light it receives and converts this light into a corresponding number of electrons, which are interpreted as shades of gray. Bright light will produce more electrons and dull light will produce fewer electrons. There are different methods (RGB, CMY and CMYG color systems) which are used to register colors in digital cameras. A CMY system produces better light sensitivity than RGB. CCD (charge-coupled device) image sensors use the CMYG color system, whereas progressive scan image sensors use the RGB color system. There are two main technologies used to develop image sensors:

  1. CCD (Charge Coupled device)
  2. CMOS (Complementary Metal Oxide Semiconductor)

CCD Technology: In CCD technology, charges from pixels are converted to voltage levels, buffered, and are then sent off the CCD chip as an analog signal. With CCD, every pixel has limited output nodes, therefore the image quality is thus very high. These cameras have been used for more than 35 years and are more sensitive to light than CMOS sensors. However CCD sensors are expensive and consume much more power than CMOS sensors. Think of the video forensics involved in the Rodney King video.

CMOS Technology: In CMOS technology, amplifiers and analog to digital converters are already integrated in the image sensor. This allows better integration and more advanced functions. Recent advancements in CMOS technology have improved the quality of images produced using these sensors. These improvements mean that the field of video forensics has advanced tremendously, especially in the last few years. 

There are, however, many other characteristics in image sensors that impact the quality of images. For example, the size of the image sensor and size of the pixels also impact the image quality. A larger image sensor with more pixels will produce higher resolution images with greater detail. A larger pixel will store more electrons from light exposure and thus will be more sensitive to light. Another factor that has an impact on image quality is the dynamic range of the sensors. An image sensor with high dynamic range will capture both dark and bright objects without “noise” on the image.

Image Scanning Techniques

Information produced by Image sensors is read and displayed using Image Scanning techniques. There are two prevalent image scanning techniques:

  1. Interlaced scanning and
  2. Progressing Scanning

Interlaced Scanning: Interlaced scanning was invented in the 1930’s and is today used in CCD based image sensors and older analog cameras. An interlaced image from a CCD camera produces one field displaying odd lines and another field displaying even lines. At any given time, only half the image lines are transmitted, first the odd and next the even lines are transmitted, then the even and odd lines are combined to form an image on every line. To allow the human visual and cognitive functions to interpret these field lines as a complete image and not separate odd or even field lines, these lines are refreshed at a certain frequency or number of frames per second. These interlacing techniques have been extensively used in analog cameras, television and VHS videos for a very long time.

Deinterlacing Techniques: There are many deinterlacing techniques that are used to show interlaced videos on either television or computer screens. An advanced technique, called motion adaptive deinterlacing, is used to produce sharp and full resolution images. This technique uses blending and calculation of motion to deinterlace the interlaced images. Another method of deinterlacing uses line doubling or interpolation to first remove either of the field lines and then double the lines of the remaining field. This method finishes the comb effect but typically has a negative impact on image quality.

Progressive Scanning: Progressive scanning is a technique that can be used in both CCD and CMOS image sensors. In Progressive scanning, values are used from every pixel of the image sensor and data is scanned sequentially. This produces a full frame image which is then sent over a network or stored. Because there is literally no flickering effect in progressive scanning, it can capture moving objects much better and is preferred in video surveillance applications.

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How Light Affects Video Recording

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How Light Affects Video Recording

Video Recording

Different lighting conditions effect a still image or video recording in dramatic and important ways.

Light has tremendous affect on the quality of a video recording or still image. For instance, details and colors of an image get lost when there is too much backlight. However with correct exposure and additional frontal light, details not visible before will emerge out in the same image. A video forensic expert should understand all aspects of lighting and how light affects a still image or video recording. The importance of understanding light allows for better forensic video analysis and applying the right video enhancement techniques. In this post we will briefly explain the different forms and direction from which light can come and how that affects the image quality. We will also explain about Color Temperature and Invisible light.

Forms and Directions of Light

For capturing a good surveillance video recording, it is important to ensure that the target area has a proper source and direction of light. If, however, lighting conditions are not ideal, and there is a video that may be a critical piece of evidence, then using video enhancement services of only the best forensic video experts may be necessary to better see what happened in the video evidence.

Most common sources or forms of light in a scene are:

  • Direct Light: Direct light can come from sources such as point source object like sunlight or from small bright object such as a spotlight. Whatever the case might be, direct light creates sharp contrast with highlights and shadow.
  • Diffuse Light: Diffuse light can come from sources such as a gray sky, from an illuminated screen, a diffuser, or light bouncing off a ceiling or other surface. With diffuse light, the object from which light comes is typically much larger than the subject. Diffused light, therefore, decreases the contrast. It also negatively affects the brightness and colors. The level of details captured will also reduce.
  • Specular Reflection: In Specular Reflection light comes from one direction and bounces in another direction by reflecting from a smooth surface. For example, Specular light is generated when light reflects off water, glass or metal, or a similarly reflective material. This type of light also reduces visibility, but it can be reduced by using a polarizing filter.

Lighting Direction

The Direction from which the light comes can significantly impact the level of details obtained. Main directions from which light can fall on the subject are:

  • Front light: Light coming from behind the camera is ideal and will result in proper illumination of the scene.
  • Side light: Light falling from the side is good to capture architectural effects but will develop shadows.
  • Back light: Backlight is created when light falls directly on the camera lens. Details and colors of the subject will often be lost when capturing images that have a higher amount of backlight.

Color Temperatures in Light

Different types of light have different color temperatures and, therefore, impact the color of an image differently. This temperature is measured in Kelvin (K) and is based on the fact that heated objects radiate this heat. Red has a lower color temperature and is the first visible light that radiates from a heated object. The color turns blue as the temperature increases. So for example, standard light bulbs will often create a brown or yellowish tone in an image. When compared with daylight they have lower color temperature of about 3000 K and will appear more reddish, where the sun is approximately 6500 K. Similarly in an industrial warehouse setting, where long tube fluorescent lights are used to provide unobtrusive light, the images will appear more green and dull in tone.

The human brain can easily cope and adjust how we perceive images from color changes occurring from different kinds of light. Cameras, however, require a special system, called a white balance system, to adapt to different sources of local illumination.

Invisible Light

Colors that fall in between 3,000 to 10,000 Kelvin temperatures are visible to the human eye. However, invisible light wavelength bands are generated by objects that have color temperature above or below these limits. These invisible light wavelengths are either called infrared or ultraviolet. Near infrared light (700 nanometers up to about 1,000 nanometers) can be filtered when a camera utilizes an IR filter. However CCD (charge-coupled device) and CMOS (complementary metal–oxide–semiconductor) cameras are insensitive to UV light. As analog cameras are sensitive to UV light, a UV filter is fitted to an analog camera and used in such situations.

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Evolution of Video Surveillance Systems

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Evolution of Video Surveillance Systems

Video Surveillance

Video Surveillance Systems Continue to Improve

The video surveillance industry is over 35 years old. It has evolved from using pure analog surveillance cameras and recording devices, which recorded videos on tape through VCR’s (videocassette recorders), to fully digital network based video surveillance systems which use high quality, high definition, network cameras to record and store videos on servers, clouds, or DVRs (digital video recorders). This change has had a deep impact on video forensics. The reasons that led to this evolution are improving computer and camera technology along with an ever increasing demand for better surveillance systems. However, in between the analog and network based digital surveillance systems exist the hybrid systems that are being used. This post will discuss the defining features of prominent video surveillance systems and outline the evolution of the modern day video surveillance industry.

Some reasons which led to improvement in Video Surveillance techniques are:

  1. Better image quality
  2. Simplified installation and maintenance
  3. More secure and reliable technology
  4. Longer retention of recorded video
  5. Reduction in costs
  6. Size and scalability
  7. Remote monitoring capabilities
  8. Integration with other systems, and
  9. More built-in system intelligence.

VCR Based Analog CCTV Surveillance Systems

Traditional VCR based analog CCTV surveillance systems used black and white analog CCTV cameras to capture videos. These analog cameras were typically connected to the VCR through a coaxial cable. The VCR recorded videos on the same VHS cassettes which were used in a home VCR (remember Blockbuster Video?). There were many problems with these cassettes, one of the largest being that they couldn’t store more than 8 hours of video and thus needed either regular replacement or constant reuse. The surveillance industry requested an increase in the the size of storage devices and to make them scalable. This led to the evolution of time lapse mode in CCTV video recording. In time lapse mode, instead of every subsequent image being recorded, every second, fourth, eighth, or sixteenth image was recorded. This storage space saving advance in technology significantly enhanced the duration of recorded videos, and the surveillance industry came up with recording specifications such as 15 fps (frames per second), 7.5 fps, 3.75 fps, and 1.875 fps. Further, to record even more cameras, people started using technologically improved devices such as quads and multiplexers.

DVR (Digital Video Recording) Based Analog CCTV Surveillance Systems

In the mid 90’s, DVRs began replacing the traditional VCR recording system. Major advantages of using DVRs over VCRs include improved video quality and increased storage space. Another advantage was that people could easily and quickly scan through surveillance videos. However, the cameras being used were still traditional analog cameras. As this technology improved, the videos were first digitized using a DVR and then stored in hard drives. As the early DVR hard drives were still very expensive and this surveillance technology new, manufacturers did not unite in the use of one recording method and so each used proprietary compression algorithms for storage. This meant that people were tied to the same manufacturer for devices used to replay the videos. However with time, cost of hard drives significantly decreased and compression algorithms such as MPEG 4 became popular.

Network DVR-Based Analog CCTV Systems

With the use of DVRs, it became possible to record videos digitally. Powered with an Ethernet port, it also became possible to transmit the digital videos over long distances, such as through the internet. Some early DVR systems allowed networking and monitoring at the same time, while others just allowed monitoring of the network transmitted videos. Also, some systems required the Windows operating system to be running on a client computer while other systems allowed monitoring through a web browser. This networked approach to video surveillance allowed for remote monitoring and remote operations (control) of the surveillance system.

There were, however, and still are disadvantages of using a DVR. DVR systems were developed by companies using proprietary hardware and software. This made DVR surveillance technology very expensive to maintain and upgrade, and difficult to share with others such as law enforcement. Furthermore, there are systemic vulnerabilities to virus and limits to scalability.

Video Encoder-Based Network Video Systems (NVR’s)

A network video surveillance system allows continuous transportation of video streams over an IP network. The first such network based video surveillance system became possible with the advent of video encoders and video servers. In these systems, surveillance videos captured using analog cameras were digitized and compressed using video encoders. Video encoders send the compressed video to a video server over an IP network using a network switch. The video server is then used to record and monitor the surveillance videos.

Network Camera Based Network Video Systems

A network camera, also called an IP camera, sends surveillance videos over an IP network and consists of no analog components. It has built in computing power — both storage for video recordings and internet connection capabilities — which can provide cutting edge video analytics preinstalled in the network camera. The images are digitized inside the camera and remain digitized throughout the system. Network based video cameras provide the highest degree of clarity. This type of  video surveillance is very prevalent and inexpensive today. In fact, companies such as Samsung, Sony, Vivotek, and many others sell these cameras (which have their own internal storage), and the quality of the surveillance videos they create is excellent.


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Image Processing for Forensic Video Enhancement Services

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Image Processing Techniques for Forensic Video Enhancement Services

Forensic Forensic Video Enhancement Services

Image processing for Forensic Video Enhancement Services

There are various image processing techniques that can be used to improve the quality of images. This post will discuss how today’s digital cameras and computer software include built in image processing capabilities and simple techniques that can be used for forensic video enhancement.


Just as a human eye adapts to different lighting conditions, a camera is also able to adjust to changes in light conditions. A camera achieves this using three parameters:

  1. Exposure time or the time an image sensor is exposed to light.
  2. Iris or aperture diameter which also manages the light coming in from the lens
  3. Gain or the digital amplification of the image level to electronically brighten images.

Increasing the gain of an image (number 3 above) will also result in increasing the “noise” in the image, but that does often help our eye see certain details of the image signal more clearly. Another alternative to enhance the image is to increase the exposure time (number 1 above), but unfortunately this can also require reducing the frame rate. All these parameters are automatically checked and calculated by automated camera tools (such as is included in most modern digital cameras, surveillance cameras, etc.). However, if the resulting image has too much noise, a forensic video expert can make use of forensic video enhancement services to improve image quality.

Backlight Compensation

Too much backlight is often difficult for a camera to handle. For example, if there is a section of the image that is very bright, the camera might think that there is too much light in the entire image and automatically reduce the iris opening or decrease the exposure time. In this case, the most important area, the area of interest, in the resulting image may be too dark and of lesser resolution quality. The effect of this is that important details can be lost in the dark. Another method known as backlight compensation introduces a mechanism to ignore specific areas of the image containing high light. Using backlight compensation, the camera will expose properly for the darker areas of the image, and the bright areas will get overexposed. Cameras can also calculate the exposure required by determining which area in an image has the maximum exposure value. Known which method is done automatically by the camera, or manually by an expert, if often vital when considering which method of forensic video enhancement services to use.

Wide Dynamic Range

In situations where both extreme light and extreme dark lighting conditions are combined, an advanced feature called wide or high dynamic range (HDR) is helpful. It uses techniques that handle a wide range of lighting conditions, such as where a shadowed person is standing in front of a backlit bright window. In such situations, the true dynamic range of a scene is the range of light levels from the darkest object to the brightest object. This technique uses different exposure levels for different areas in the image. Although this technique often works well, using wide dynamic range can pose certain problems:

  1. Different noise can occur in different regions, especially dark regions which can have a high level of noise.
  2. In images with many different lighting conditions, large artifacts might show up in between pixels of different lighting conditions.
  3. Because this technique often requires taking two pictures of the image, one quickly followed by the next, and then combining the results, if quick movement occurs at the time when the two images are captured, the final image can contain strange errors due to the movement and location changes of the objects.
  4. Colors can be very weak and regions with different light may receive very low dynamic range.

Bayer De-mosaicing

All digital cameras use a process of de-mosaicing in order to process raw image and convert it into a high quality color image. As each pixel simply records the illumination behind one of the color filters, values from neighboring color pixels are used and interpolated to calculate the actual color of that pixel. This is done using the process of de-mosaicing, which uses an algorithm that converts the raw image into high quality color image.

White Balance

After color interpolation, white balance is performed to ensure correct color balance. The neutral (black, gray, white) colors stay neutral regardless of the illumination. In network cameras indoor and outdoor settings are used to manage the white balance. In cameras having an auto white balance function, two or three different gain factors are used to amplify the red, green, and blue signals.

Sharpening and Contrast

Digital images can be enhanced using Digital Sharpening and Contrast Enhancement. Digital sharpening is the process of increasing contrast at the edges by lightening the light pixels and darkening the dark pixels. Contrast enhancement affects the overall image, by changing how the original pixels are mapped on a display screen.

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Facial Recognition Using 3D Mug Shots – Future of Forensic Surveillance

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Facial Recognition Using 3D Mug Shots – Future of Forensic Surveillance

Facial Recognition

Turning a 2D photograph in 3D for Facial Recognition

Facial recognition is an essential element of video forensics. Our human visual cognition system is attuned to natural facial recognition, in fact we easily recognize human faces even in tough visual environments, such as bad light conditions or different pose variations. Many technologies have also been developed that allow computers to recognize faces. Although many of these facial recognition systems have been used for a long time they are typically based on 2D image facial recognition. However, 2D image facial recognition systems pose many challenges which are directed related to its data variability. When a surveillance camera records a face, it’s usually at a strange angle – not the standard straight on image captured in a standard ID, such as a Driver’s License or passport photos. A 2D image based facial recognition system is an inadequate tool for matching faces captured from different angles. Other challenges to facial recognition may include challenges due to pose variations, bad lighting conditions, occlusions and facial expressions.

Facial Recognition

To overcome these challenges scientists have been working aggressively to develop computer vision systems that can process and analyze 3D faces exactly as the human vision does. Also called the 3D mug shot, this is an interesting technology which is recently being used by police officers in Tokyo. It creates a map of a face that can be used to match surveillance images — even at strange angles. From April 2016, Tokyo’s 102 Metropolitan Police Department Stations will place 3D cameras that will capture faces and record unprecedented facial information for their 3D identification stations. Besides being used for forensic and surveillance purposes, these systems can also be used in biometric machines, improve human–computer interaction (HCI), facial surgery, video communications and 3D animation.

Basic concept of 3D Facial Recognition

Facial Recognition

Computers can now adapt facial recognition software to compare against different facial expressions

The 3D facial recognition programs attempt to recover facial 3D shapes from cameras and reproduce their actions. Furthermore, the software also attempts to recover these facial shapes under multiple pose and light variations. The fields of computer vision and computer graphics are closely related to this technology of facial recognition. It requires high tech knowledge of capturing and processing human geometry. Such programs should be capable of using techniques for 3D reconstruction of geometric shapes. Way back in 2010 computerized tests were made on about 1.6 million mug shots to pick someone from these mug shots. The advanced algorithms achieved this with an accuracy of 92 percent. Tests were also run on photos of people who were not looking directly at the camera. With such advanced technologies, video forensic experts can use forensic video analysis and convert low quality surveillance images into powerful evidence with unprecedented accuracy.

Geometric and Topological Aspects of the Human Face

Facial Recognition

Resistance is Futile

Some notable geometric and topological features of a human face are considered as distinguishing features of any human face. In effect they pose both a challenge and an opportunity to the field of 3D face recognition. The following points discuss the influence of these aspects and the challenges they pose to 3D face recognition:

  1. Changes in Human face: The human face can change as a result of factors such as age, weight loss, weight gain and facial expressions. As distinguishing 3D shape variations of human face among different individuals are statistically small, these changes pose serious challenges in the field of 3D facial recognition. Besides changing the geometry, some changes such as mouth opening can result in topological changes to 3D facial structure as well. However, following three aspects of human face have helped overcome these challenges and also helped in the development of rigid approaches for 3D face recognitions systems:
    1. The anatomical structure of the face remains unchanged, especially in the case of changes related to human expressions.
    2. Some facial regions such as the nose and forehead are less affected by change in expression. These regions are also called the semirigid regions.
    3. Depending on the facial expression, some other facial regions besides the semirigid regions, will be less deformed.

Fiducial points in a Human face: Some detectable fiducial points in a human face are eye corners, the mid point between the eyes, the tip of the nose and its two lower corners, the furthest chin point, and mouth corners. These fiducial points are often used to establish point-to-point correspondence between two or more facial scans. They also act as standard locations from where other local features are extracted. This helps in the process of facial feature matching and recognition. However this can pose a challenge in case of deformed surfaces. In such cases the issues can be resolved by first establishing relation between these fiducial points.

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Pick Up Patterns in Microphones – Forensic Audio Analysis

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Pick Up Patterns in Microphones – Forensic Audio Analysis

Forensic Audio Analysis

Forensic Audio Analysis is a key part of Digital Forensics

Microphones convert acoustic vibrations or acoustic energy into electrical energy for the purpose of amplification or recording. These acoustic vibrations vary largely based on characteristics such as waveform, phase characteristics, frequency response, dynamic range and attack time, to name just a few. Different microphones use different techniques to reproduce above mentioned characteristics. Therefore, for quality forensic audio analysis and forensic audio enhancement, a good knowledge of the different microphones used today is essential. This post will discuss and classify microphones based on pickup patterns and directional response characteristics. Critical forensic information regarding the direction of a sound wave recording can be analyzed based on pick up patterns of microphones. This information will also help you the user to take informed decisions while purchasing microphones.

In a microphone, acoustic energy is converted from air pressure into a small electron flow. An audio forensic expert should understand the basic mechanism, features and specifications of all the different microphones used today when conducting a proper forensic audio analysis.

The three main components of all microphones are:

  • Diaphragm: It is a lightweight moving surface that gets excited by the acoustical waves. The sound wave causes the diaphragm to vibrate in sympathy, which as a result outputs a corresponding mechanical signal.
  • Transducer: A transducer converts the mechanical signals generated by the Diaphragm into electrical signals.
  • Casing: It helps control the directional response of microphone. It also provides mechanical support and protection to the diaphragm and transducer.

Microphones are Categorized Based on Pick up Patterns of Sound Waves

Microphones can be distinguished based on pick up patterns they employ. They can use single or multiple pickup patterns and are classified in the following types:

  • Omnidirectional Microphones
  • Bidirectional Microphones
  • Unidirectional or Cardioid Microphones
    1. Single-Entry Cardioid Microphones
    2. Three-Entry Cardioid Microphones
    3. Multiple-Entry Cardioid Microphones
    4. Two-Way Cardioid Microphones

Omnidirectional Microphones

In omnidirectional microphones, diaphragms are exposed to acoustic waves at the front side of the microphone, although the pickup is equal in all directions. This category of microphone is also called spherical microphones. Unlike unidirectional microphones, omnidirectional microphones are capable of achieving very flat, smooth frequency response, eliminating phase cancellations. Also the pickup sensitivity of signals coming in from rear or sides will be the same as the front. This helps in picking room characteristics or conversations around a table, but can have a negative effect in noisy environments. For recording higher frequencies, omnidirectional microphones should have a smaller diameter. If at certain frequencies the diameter of the diaphragm is one-tenth of the diameter of the wavelength, the response will start to diverge.

Bidirectional Microphones

The field patterns of bidirectional microphone form a figure 8 like pattern and picks acoustic waves from both the front and back equally well. In bidirectional microphones, background noise in a reverberant field will be approximately 67% lower than omnidirectional microphones. They are commonly used to pick two conversations happening on opposite sides of a table. The pickup distance will be 1.7 times greater in a direct field than that of omnidirectional microphones. The pickup cone angle of a perfect bidirectional microphone, is 120 degrees off the rear and 120 degrees off the front for a frequency of 6dB. This angle is directly proportional to the frequency and will become narrower with increased frequency.

Unidirectional Microphones

Unidirectional microphones have a pickup pattern that is heart shaped and are thus also called cardioid microphones. They pick up sounds coming from front of the microphone with greater sensitivity. The average ratio between front and back of the microphone is 20:30 dB. For mid frequencies this ratio will change to 15:30 dB and for extreme frequencies it can be as little as 5:10 dB. This phase inversion is the result of additional distance a wave has to travel to reach the back of the diaphragm. They are the most commonly used microphones as they discriminate between signals and random unwanted noise.

Unidirectional or Cardioid Microphones are further classified on the basis of holes which allow the sound waves to enter the rear cavity of the microphone:

  1. Single-Entry Cardioid Microphones: In single-entry cardioids microphones, the rear entrance port is located at one distance, usually within 1.5 inches from the diaphragm. Eg. Electro-Voice DS35, Shure SM81, etc.
  2. Three-Entry Cardioid Microphones: Sennheiser MD441 is a three-entry cardioid microphone. Its rear entry is about 2.8 inches from center of Diaphragm, mid frequency entry is 2.2 inches and high frequency entry is about 1.5 inches from the center of diaphragm.
  3. Multiple-Entry Cardioid Microphones: Multiple-Entry Cardioid Microphones have many rear entrance ports. Electro-Voice RE20 Continuously Variable-D microphone is an example of this type of cardioid microphone.
  4. Two-Way Cardioid Microphones: In two-way cardioid microphones, response range is divided in between high frequency and low frequency transducers. It produces more linear frequency response at sides and allows constant discrimination at the rear of microphone.
Forensic Audio Enhancement

Forensic Audio Enhancement

NCAVF – Audio Forensic Expert & Audio Enhancement Services

NCAVF provides expert forensic audio analysis and audio enhancement services. If you want a consultation with our audio forensic expert or require audio forensic services, then please contact us at 213-973-7811.


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Government is Using Microphones to Record Us

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The Government is Using Microphones to Record Us


The Government is Using Microphones to Record Us in Baltimore buses

In the 1960’s, James Bond made planting a “bug” (or hidden microphones) and wiretapping exciting. Bond would sneak in a hotel room, seduce the woman, and place a tap on the phone all without spilling his martini. As consumers of action spy movies, we ate it up. But back in real life, where we are not in the movies, everyone was comfortable knowing that their own phones were safe from being tapped. Why? Well we had the Fourth Amendment to protect us from an illegal search such as a wiretap. But not anymore. City buses in Baltimore, MD have been outfitted with microphones and are wirelessly transmitting everything being said to the government. The government is using microphones to record us. Private conversations are no longer private.

Recently, The Washington Post wrote an article about how the Maryland Transit Administration (MTA) has been using these clandestine audio recording devices since 2012. Now, nearly 500 out of their 750 buses are equipped with these audio recorders. Baltimore officials claim the microphones and transmitters are intended to be used to pick up on driver errors, altercations, or attacks on the buses. But are these reasons really worth a forfeiture of our Fourth Amendment rights? Further, it’s important to note that even though buses are in public, there is still an expectation of privacy for a conversation between people riding the bus.

Does the Senate Approve?

Senator Robert Zirkin (D-Baltimore County) seems to be opposed to the recording devices. “What [the MTA] is doing is a mass surveillance. I find it outrageous, “ Zirkin said. “I don’t want to overstate it, but this is the issue of our generation. As technology advances, it becomes easier and easier to encroach on people’s civil liberties.” In light of this, Zirkin, and others, have voted for a measure aimed at questioning whether or not these bus microphones are truly a good thing for the public. It is yet to be seen, however, whether their attempts to forestall the audio recording will be successful.

Baltimore, however, is not the city with public transportation that’s violating riders’ civil liberties and privacy rights. Buses in Los Angeles, San Francisco, Boston, and other cities around the country have also been fitted with devices intended to record activities or conversations.

Private Citizens Wiretapping

With big government throwing our rights to the wind, it begs the question, “what would happen if a private citizen tried the same thing by setting up a hidden microphone to record a conversation?’ Simply put, he’d likely go to prison. Remember Anthony Pellicano? He was a high-profile LA private investigator convicted in 2008 for wiretapping celebrities. Pellicano was sentenced to 15 years and fined $2,000,000. How about Christopher Chaney? In 2012, he was also convicted of wiretapping celebrities. The list of civilians convicted goes on, while the government seems to continue these actions with impunity. It should be noted that this is typically the case of audio recording. Video recording, like recording the police, is a whole other topic.

While Sen. Zirkin and others try to fight the use of government clandestine recording devices, we, as citizens, need to become acutely aware that issues such as this have the potential to become much more prevalent. The first video cameras were large and heavy, now they can be hidden in the bill of a baseball cap. Microphones and digital audio recorders can be as small as a hair and hidden almost anywhere. Even our own smartphone microphones can be turned on by the government and used for eavesdropping. As citizens, it’s crucial we pay attention to not only the direction that the technology evolves, but to how our own government may be violating our privacy rights and civil liberties.

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Video Enhancement Services for Fingerprint Recognition

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Video Enhancement Services for Fingerprint Images

Video enhancement services is not just limited to surveillance video or police body cameras. Our palm and finger skin have a unique flow like pattern consisting of ridges and valleys. These ridges and valleys help increase friction thereby assisting us grasp objects. Also called as frictional ridges they help enhance the sensing of surfaced textures. This frictional ridge pattern is unique to every finger such that even identical twins can be differentiated on the basis of their fingerprints. As can be seen in the image below, the dark lines represent the friction ridges, while the white space represents the valleys. Fingerprint identification is based on the location of ridge ending and bifurcations along a ridge path.

Video Enhancement Services

Sometimes fingerprints need to undergo video enhancement services to make a match

It was during the late 19th century that the scientific study of personal identification based on fingerprints received its due importance. Fingerprints are now one of the most widely used biometric format for person identification. Law enforcement agencies and forensic experts use fingerprints to identify criminals as criminals often tend to leave their fingerprints at crime scenes.

This article provides a brief summary of the history, standards and challenges faced in fingerprint recognition. It also explains the video enhancement services available and image enhancement techniques used for fingerprint analysis.

History and Standard Regulations for Fingerprint Recognition


Sir Francis Galton, Henry Faulds and Edward Henry are amongst the notable figures that helped establish the scientific basis for using fingerprints to recognize people. In late 1960’s with the advent of computers, a subset of Galton Points also called minutiae, helped expand the science of fingerprint identification. Recognizing its impact on forensic science, in 1969 FBI contracted National Bureau of Standards (NBS) now the National Institute of Standards and Technology (NIST) to automate the process of fingerprint matching. Later in 1975 FBI funded the development of a prototype fingerprint reader and classifier.

Over the course of next few years NIST further developed the study of fingerprints and developed the advanced M40 matching algorithm for narrowing the human search. By 1981, 5 Automated Fingerprint Identification Systems (AFIS) had been deployed in USA. In 1999 major components of Integrated Automated Fingerprint Identification System (IAFIS) were operational. By 2003 NIST developed accurate multiple tests under the Fingerprint Vendor Technology Evaluation (FpVTE) which helped evaluate the accuracy of fingerprint recognition systems.


Consistent efforts resulted in various standards to standardize the content, meaning, and representation of fingerprint data interchange formats. Standards developed include the ANSI/INCITS 381-2004 Finger Image-Based Data Interchange Format, ANSI/INCITS 377-2004 Finger Pattern Based Interchange Format, ANSI-INCITS 378-2004 Finger Minutiae Format for Data Interchange, ISO/IEC 19794-2 Finger Minutiae Format for Data Interchange, ISO/IEC FCD 19794-3 Finger Pattern Based Interchange Format, and the ISO/IEC 19794-4 Finger Image Based Interchange Format.  ANSI NIST ITL 1-2000 Data Format is a standard for the Interchange of Fingerprint, Facial, & Scar Mark & Tattoo (SMT) Information. Electronic Fingerprint Transmission Specification (v7.1) and Electronic Biometric Transmission Specification (v1.0) are specific implementations of ANSI NIST ITL 1-2000 used by the FBI and DoD. Other standards also associated with ANSI NIST ITL 1-2000 are the FBI’s Wavelet Scalar Quantization (WSQ) and Join Photographic Experts Group 2000 (JPEG2000) which are both used for the compression of fingerprint images.

Challenges Faced in Fingerprint Recognition

Here we are discussing few problems associated with fingerprint recognition. Further research on these challenges can be studied under domains such as video enhancement, image processing, computer vision, statistical modeling, cryptography and sensor development.

Video Enhancement Services

Video enhancements services can sometime be utilized in order to complete a fingerprint evaluation

  1. Rounded shape of fingers, rolling and improper pressure on reading devices cause difficulty in capturing complete fingerprint on touch based sensors.
  2. Difficulty in capturing fingerprints on touch based sensors, caused due to dry, wet or dirty finger, cuts on fingers or finger flattening.
  3. Overlapping caused due to improper finger placement by user results in overlapping impressions.
  4. Altered, fake and latent fingerprints also results in challenges faced by forensic experts.
  5. Lack of interoperability due to difficulty in building a single finger recognition system which can be used by all.


Video Enhancement Services and Techniques for Fingerprint Image Enhancement

Below are some video enhancement techniques used to enhance the quality of fingerprint images:

  1. Short Time Fourier Transform (STFT) Analysis is an image enhancement algorithm which requires STFT analysis and contextual filtering. As a result it also provides an energy map which is used to compute the angular coherence.
  2. Pyramid based filtering uses two symmetries to  model and extract the local structure in a fingerprint. These two symmetries are called parabolic and linear symmetry.
  3. Curved Gabor Filters are used to enhance curved structures in noisy images by locally adapting the shape to a direction of flow. Gabor filters play an essential role in the field of image and video enhancement.
  4. Histogram Equalization, Fast Fourier Transform and Image Binarization also involves a two stage process requiring minutiae extraction as the first step and minutiae matching the second. Minutiae extraction includes image enhancement, image segmentation and final extraction while minutiae matching includes minutiae alignment and match processes.Video Enhancement Services
  5. Oriented Diffusion Filtering and Curved Gabor Filters is used to enhance low quality fingerprint images. After estimating the local orientation of ridge and valley flow the oriented diffusion filtering is performed  followed by locally adaptive contrast enhancement step.
  6. Discrete Fourier Transform (DFT) and Histogram Equalization uses a combination of Discrete Fourier Transform (DFT) and histogram equalization to reconstruct the information of the fingerprint image.
  7. Coherence Diffusion Filter and Gabor Filter uses a combination of spatial domain two dimensional Gabor filter and diffusion coherence filter.
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Using Face Recognition Methods for Forensic Video Analysis

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alt=”Forensic Video Analysis

Facial recognition is often an essential aspect of forensic video analysis.

Facial Recognition for Forensic Video Analysis

Facial recognition experts are highly trained Forensic Video and Image analysts who specialize in the fields of human diversity, anthropology and forensic analysis. Even though we recognize faces every day, but to analyze and replicate this same behavior of a human brain through scientific procedures and then to testify in court that a suspect is a criminal, is not as easy as we might think. This requires the expertise of a facial recognition forensic analyst. They use techniques such as Morphological analysis, Anthropometric, Photographic Superimposition and 3D–3D comparison to recognize faces in a surveillance camera shoot.

With the growing use of video surveillance cameras, the need for facial recognition in forensic video analysis is also growing. There are cameras fitted almost everywhere, on roads, malls, parking lots, lobby, homes, offices, airports, entry and exit doors of every other building. Usage of facial recognition for forensic analysis dates back to the end of 19th century, when Alphonse Bertillon, a French police officer, first used anthropometry for the purpose of facial recognition. In literal terms anthropometry is the study of human body measurements especially on a comparative basis. However forensic anthropometry for the purpose of facial recognition, involves a careful comparison of morphological characteristics of some peculiar anatomical parts (such as eye, nose, mouth, and ear) of the head.

As explained above facial recognition is an essential and difficult aspect of forensic video analysis. What make it even more difficult are limitations such as disguises to hide identity and poor quality of surveillance camera footage. Though special forensic enhancement software are available that enable demultiplexing, frame averaging, duplication, video level adjustment, magnification, highlighting, and obscuring of multiple subjects and specific areas. But even post enhancement, a forensic specialist, preferably an expert in the fields of biology/anthropology/human-diversity, is required to recognize faces, especially when the original quality of surveillance videos or images is very low.

Face Recognition methods for Forensic video analysis

Forensic facial identification involves comparison of two or more faces to recognize the facial image in question and determine the true facial identity. Amongst the traditional approaches used for face recognition are 3 well known techniques. These are Morphological analysis, Anthropometric and Photographic Superimposition.

  1. Morphological Analysis – This is a very old approach of facial recognition. It involves comparison and identification based on individual features of a face. Several classifications of different forms of facial regions and types exist. These classifications aim at meticulously classifying different facial traits, such as, facial outline shapes, hairline shapes, mouth, nose, etc. Some classifications can consider up to 40 facial traits. They are designed to promote a consistent, systematic, and scientifically comparable evaluation of facial features as well as making individual variations and population differences emerge from a seemingly unremarkable visage. It can guide the expert in making a decision about the final match.
  2. Anthropometric approach – This approach is described as the quantification of physiological proportions between facial traits, dimensions, ratios and angles to measure specific characteristics of a face. It is generally used for facial comparison of similar face orientations. Even slight differences in orientation, facial expression, lighting conditions, camera distortions, camera positioning and aging can impact the end results and will require proper video enhancement. The method proposed by Alphonse Bertillon was based on anthropometric measurements. He developed a taxonomy, called as portrait parle or spoken portrait, which described the physiological features of the head, nose, forehead and ears. This combination of anthropometric measurements and the spoken portrait was called Bertillonage and was very soon adopted by judicial systems of that time. 
  3. Photographic Superimposition – This technique involves comparison of facial traits by superimposing them on each other. Forensic experts take pictures or videos of the suspect with closest possible head orientation of the criminal, and then compare facial characteristics through superimposition and numerical analysis of two images.  Favorable recording conditions are essential and this technique is usually impractical, especially if long time has elapsed between the occurrence of crime and the facial recognition or forensic analysis. 
  4. More modern approach to facial recognition techniques involves comparison in 2D or 3D structures. With technology and software advancements the shift is also towards automating the identification approaches. Forensic experts are using quantitative identification procedures like 2D-2D, 3D-2D and 3D-3D comparison techniques to identify the criminals. These are more reliable and accurate techniques.
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Forensic Audio Analysis and Weapon Recognition

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Forensic Audio Analysis and Weapon Recognition

Forensic Audio Analysis

Forensic Audio Analysis can be used to ID firearms

Modern technology in the field of forensic audio analysis makes it possible for an audio forensic expert to be able to recognize a firearm, sometimes by just listening to the sound of firing. Trained audio forensic experts recognize the different type of firearms, the different barrel lengths, types and weights of ammunition, that result in varying bullet speeds and totally different Sound Pressure Levels (SPL) or audio signals. In this post we will share insights to the Sound Pressure Levels (SPL) and Bullet Speed of highly used Revolvers (.357 Magnum and .38 Revolver), 9 mm Pistol and Rifles (.30-.60 Rifle and .223 Rifle) along with bullets like Winchester Silver Tip Hollow Point, Winchester Full Metal Jacket, Winchester Soft Point and Hollow point.


A Smith & Wesson .357 Magnum Revolver (Model 13) with a 3 inch barrel, when fires a Winchester Silver Tip Hollow Point bullet weighing 9.4 grams will result in a bullet speed of 362.4 meters per second and a Sound Pressure Level (SPL) of 155.4 decibels (when recorded at 1 meter and at an angle of 90 degrees). However when the same revolver fires a Remington Jacketed Hollow Point bullet weighing 8.1 grams results in a bullet speed of 427 meters per second and a SPL of 158.5 decibels. A Winchester Silver Tip Hollow Point bullet weighing 7.1 grams when fired with a .38 Smith & Wesson Revolver (Model 10) having a barrel of 2.5 inches, will result in a bullet speed of 269.7 meters per second and a SPL of 153 decibels. The same bullet when fired with 4.0 inches barrel will produce a bullet speed of 287.1 meters per second and a SPL of 151.4 decibels. A .38 Smith & Wesson revolver (Model 60) when fires a Winchester Silver Tip Hollow Point bullet weighing 7.1 grams, with a 2.5 inch barrel will result in a bullet speed of 250.2 meters per second and Sound Pressure Level of 155.4 decibels.

Pistol, 9mm

A Winchester Full Metal Jacket bullet weighing 7.5 grams, when fired with a 9mm pistol of Sig Sauer P-226 make and 4.5 inch barrel will result in a bullet speed of 333.8 meters per second and SPL of 153.7 decibels. The same bullet if fired with a 9mm pistol of Colt make and Model 2000, will have a bullet speed of 345 meters per second and SPL of 153.7 decibels. A Winchester Silver Tip Hollow Point bullet weighing 7.5 grams, when fired with a 9mm pistol of Sig Sauer P-226 make and 4.5 inch barrel will result in a bullet speed of 344.7 meters per second and SPL of 152.5 decibels. This same bullet when fired with a 9mm pistol of Colt make and Model 2000, will have a bullet speed of 357.5 meters per second and SPL of 153.1 decibels. A Winchester Full Metal Jacket bullet weighing 9.6 grams, when fired with a 9mm pistol of Sig Sauer P-226 make and 4.5 inch barrel will result in a bullet speed of 280.7 meters per second and SPL of 152.4 decibels. The same bullet but of weight 9.5 grams if fired with a 9mm pistol of Colt make and Model 2000, will have a bullet speed of 288.3 meters per second and SPL of 151.9


When a Winchester Soft Point bullet weighing 8.1 grams is fired with a .30-06 Rifle of Winchester 70 make and barrel 22 inches, the result is bullet speed of 889.1 meters per second and 160.8 decibels of SPL. When the same rifle fires the same bullet having a weight of 10.7 grams the resulting bullet speed is 827.5 meters per second and 160.1 decibels SPL. The last firearm we have covered is a .223 Rifle of Colt M16A1 make having a barrel of 21 inches, when fires a Winchester Hollow point bullet weighing 4.1 grams, will result in a bullet speed of 803.8 meters per second and SPL of 156 decibels.Post forensic enhancement, when we use professional Forensic Audio Analysis skills and combine it with this type of accurate statistics we can decipher many insights like the type of bullet fired, the exact location of gunshot and the type of firearm used. These insights help us in solving many crimes and reconstructing crime scenes with high precision accuracy.


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