3M Cogent, Inc.White Paper

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FacialRecognition

Biometric Technology

Automated Facial Recognition: Turning Promise Into Reality

Once the province of fiction, automated facial recognition has in just a few years emerged as more than a promise, but a distinct, doable reality.

Over millions of years, the brain has evolved to immediately identify and focus on faces, distinguish features, and search our brain’s “database” to match what we see with memories of previous faces. Teaching cameras and computers to “process faces” is the foundation of facial recognition technology:

• Identification and authentication can be made across a variety of contexts.

• As a no-touch system, all it requires for identification or authentication is that a person “shows up.”

• Existing monitoring and surveillance systems can be enhanced to automatically identify individuals.

• Unlike other biometrics, facial recognition is an ability all humans have, so operators can easily verify questionable matches.

Inherent ChallengesOur brains find, process, and store face information with so little effort or attention that the sheer complexity of the task is hard to imagine. Translating that task into something a computer can do poses formidable obstacles:

• Faces have too much information—human faces are highly complex patterns that differ in subtle ways, even more so than other biometrics, such as fingerprints and irises.

• Face information is too volatile—our faces do not stay the same over time, altered as they are by age, weight, hairstyle, cosmetics, facial hair, paraphernalia, and transitory facial expressions.

• The information is a moving target—images don’t always capture the same face. Camera angle, lighting conditions, image quality, background, resolution, and partial images all produce different information sets, even for the same exact face.

In recent NIST studies of facial recognition algorithms, most were easily overcome by these hurdles. Some software delivered false-reject rates of up to 43 percent using photos of subjects taken just 18 months apart. And a 45 degree angle rendered nearly all facial recognition systems helpless. If simply ducking one’s head or making a face—or just getting older—is enough to frustrate the system, then facial recognition technology will require dramatic innovations to become reality.

Making It RealThe computational task is truly daunting. However, algorithms and software engineering are only half the problem. Facial Recognition Systems are also systems. They exist in the real world and, as such, they must:

• Integrate into existing information technology, security, and access control systems

• Work with legacy data and databases

• Adapt to meet the unique and evolving needs of disparate organizations

• Interface effectively with human operators

Making automated facial recognition into a reality is more than just an abstract engineering problem (“can the system work?”), but a systems problem (“can the system work for us?”).

3M Cogent Facial Recognition Systems (FRS)3M Cogent has leveraged more than 20 years of biometrics systems engineering to invent unique computational solutions that can match faces with unprecedented accuracy, in spite of a wide range of conditions and variations. The 3M Cogent FRS does not need perfect imaging conditions, but quickly adjusts and adapts to face or image changes without losing accuracy.

3M Cogent’s Facial Recognition System involves more than just industry-leading facial recognition algorithms, but incorporates lessons from customizing biometric identification systems for all levels of government worldwide. 3M Cogent develops and builds the entire FRS system—from algorithms to system integration—to meet the unique requirements of each customer. Accurate, scalable, customizable, usable—3M Cogent’s Facial Recognition Systems are turnkey solutions that allow organizations to focus resources on their core mission.

NIST Multiple Biometric Grand ChallengeIn December, 2008, just four years after it was released, the 3M Cogent Facial Recognition System squared off against the nine other industry-leading products in the National Institute of Standards and Technology Still Face Challenge Problem at the Multiple Biometric Grand Challenge (MBGC). 3M Cogent facial recognition software led in measured accuracy against all other vendors for both controlled and uncontrolled facial image comparisons. Even in adverse lighting, image, and pose conditions, the 3M Cogent algorithms consistently scored in the top tier for high accuracy (90+%) at a low false accept rate (.001%).

Seeing Faces The human brain brings millions of years of evolution to the task of “seeing” and identifying a face. Computers were engineered to “see” and process information in vastly different ways than the brain.

For computers to “see” and identify human faces, several computational steps, each with unique and challenging obstacles, have to be successfully completed. Errors introduced in any stage accumulate, reducing the dependability of the final result.

Global, Local, and Point-Based Features3M Cogent algorithms graph facial features across three data sets:

• Global as a graph mesh

• Local as groups of reference points

• Individual characteristic points

Each of these three comparisons generates likely matches with similar images in the database. The 3M Cogent scoring algorithms are constantly refined through training the system—both true and impostor results help “teach” the system by fine-tuning the scoring mechanism.

Step 1: Finding the FaceSystems process numbers, so the first step in identifying and authenticating faces is to teach the computer to find the face in an image, a task much thornier than it sounds. Dark pigmentation, poor lighting, low resolution, facial paraphernalia, and camera angles can all “hide” a face mathematically. Where a person sees a face, a computer may see numbers indistinguishable from the background, unless sophisticated algorithms can compensate. Finding a face also means separating the face from the rest of the image and, in the case of video, tracking the face as it moves and changes from frame to frame.

3M Cogent technology employs sophisticated face-finding algorithms, able to accurately discover and isolate a face no matter what the person’s race, camera angle, lighting, and other face-hiding distractions. The software can then evaluate several images of the same face to find the most useful image for processing and matching.

Step 2: Normalizing the FaceBefore any matching can take place, faces must be adjusted to present a common “data set” as defined in international (NIST) standards. To a human observer, this data set looks like a straight-on, well-illuminated image of a forward-looking face, both eyes open, and undistorted by facial expressions or hidden by facial paraphernalia. Since not all face images are quite this perfect, the software must normalize the image.

Step 3: Extracting InformationFaces, even after normalization, are a mountain of data, but not all that data is necessary to correctly match face images. “Feature extraction” is the signal processing mathematics that 3M Cogent algorithms use to find and isolate the most useful numerical information in a face, information that best accounts for the variation between faces.

3M Cogent algorithms then perform mathematical transforms to translate the information into a set of relationships that can be most accurately and quickly compared with other face data sets. Once all images, including images in a database, have passed through these first three steps, the system is ready to match faces.

• 1:1 Matching—Used primarily in access control or other security situations, 1:1 matching mode compares two face images to determine if the person in both images is the same person.

• 1:Few Matching—Used to allow or disallow access, 1:Few matching uses a Watchlist module to compare a face image to a limited number of face images predetermined for identification.

Step 4: Matching FacesThe “recognition” part of automated facial recognition involves comparing the extracted data of an unknown face with the extracted data of known faces. The face data sets are compared, differences in data are identified, and, when the data are similar, a probability (score) is assigned that the data do, in fact, show the same face. The sophistication and accuracy of the earlier algorithms are critical to providing correct and comparable data. The completeness of the statistical analysis determines how dependable the final match really is.

3M Cogent facial matching algorithms have evolved from two decades of biometric sampling and matching research and development. 3M Cogent matching software uses unique statistical comparison and testing models from other proven biometric comparison products that achieve industry-leading accuracy in the final score.

Score FusionUsing proprietary statistical models with lengthy and ongoing training with real-world results, the 3M Cogent algorithms calculate the overall likelihood that an individual face match can be made using the three sets of data.

By remapping global, local, and point-based comparisons into a final matching score, score fusion powerfully reduces the deleterious effects of poor quality images, bad lighting, and the myriad of other variations that frustrate digital facial matching.

Matching Modes3M Cogent systems provide three modes for matching faces:

• 1:N Matching—The 1: N matching mode compares a face image against all images in a database. It allows for a centralized agency or inter-agency search and is fully scalable to meet an organization’s evolving requirements.

Facial Recognition System Searching Workflow

Four Major Steps of Facial Recognition

four steps to

Seeing a Face

Integration3M Cogent systems are built using standardized languages and tools—Service Oriented Architecture (SOA), .NET framework, and Oracle 11g database management—to allow for efficient integration with existing systems, programs, and databases. With SOA, the 3M Cogent FRS can process ISO or ANSI/NIST-compliant transactions to/from existing, legacy biometric systems, including legacy facial recognition systems.

Proven Biometric SolutionThe ultimate test of any Facial Recognition System is its usability as a complete solution. Just as the software must recognize variable and constantly changing faces, so the system has to be deployed across different organizations with disparate and ever-evolving requirements.

The 3M Cogent solution enables cost-effective and efficient integration with existing systems, high performance, adaptability, and ready usability by human operators and administrators.

3M Cogent biometric systems are designed to fit, adapt, and evolve to the needs of government and commercial organizations. In both software and hardware, 3M Cogent biometric systems scale linearly with the biometric matching and computing needs of the organization. 3M Cogent’s Workflow Manager allows for rapid administrator customization of the system’s workflow. Administrators can easily adapt the system to new business rules while maintaining legacy connectivity, thus minimizing risk and cost to the organization.

Built on two decades of innovative multiple biometrics systems experience, research, and industry leadership in everything from biometric input devices to software to the design and installation of distributed biometrics systems for business, government, and law enforcement agencies. 3M Cogent’s facial recognition aces the ultimate test—the real world test.

3M Cogent is continuously pushing the frontiers of facial recognition technology with innovative and customized products:

• Facial recognition-enabled mugshot management—Available as part of a complete or standalone system, 3M Cogent’s Mugshot System allows law enforcement personnel to securely access the mugshot database from a desk or crime scene and find mugshot matches using simple descriptions or digital images of the perpetrator.

• ID document issuance—For driver’s licenses and other forms of government and commercial identification, 3M Cogent provides immediate face image capture, normalization, and 1:1 and 1:N identification.

• Personal PC facial recognition-enabled login—3M Cogent’s BioTrust replaces unwieldy PC and Web password management with fingerprint or facial recognition biometrics. Using a PC’s camera, BioTrust can store passwords and match the user’s face to a predetermined image to log the user in to a computer or Web site.

Does it

Work for You?

Security Systems Division3M Cogent, Inc.639 North Rosemead BoulevardPasadena, CA 91107 U.S.A.1-626-325-9600www.cogentsystems.comcgtinfo@cogentsystems.com

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