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Introduction to the facts, features and benefits of Face recognition (as 100KB word document)

FLASH PRESENTATION of the technology and uses (9 MB)

The advanced features and demonstration applications included in the Acsys FRS Discovery package give you first-hand experience of our leading-edge face recognition technology.

FRS Discovery provides the following leading-edge functionality:








Scientists believe face recognition tech can protect your identity PDF Print E-mail
By Wolfgang Gruener
Recommend article:

Houston (TX) – A research team at University of Houston believes that a new, reliable approach to face recognition is able to tackle the growing threat of ID theft. The group of the school’s Computational Biomedicine Lab (CBL) has come up with a complex technology that promises to correctly detect more than 99% of individuals.

The threat of ID theft is very real these days and has become a part of our life. Whenever we have to represent ourselves, there is some form of identification involved, mostly in number codes, pictures, access names and passwords. Most of these representation types can be manipulated with a certain effort, which requires individuals to keep a close eye on where and how their ID data is used.  

Biometric types of identification are becoming more and more available, such as fingerprint sensors on notebooks that take over the function of filling out forms that require access names and passwords. There are also more sophisticated biometric technologies, such as face recognition, which however have been plagued by low detection rates – mostly due to the fact that the appearance of a face can change quickly – for example by changing a hairstyle or temporary changes such as sunburns or band-aids.

The Houston CBL research team around professor Ioannis Kakadiaris believes to have found a critical key to dramatically elevate the accuracy of face recognition. Their technology, consisting of 3D sensors, different cameras as well as the “URxD” face recognition software takes a 3D snapshot of an individual to create a unique biometric identifier. The group claims that first test have shown that the technology is able to correctly identify 99.6% of entries with a false accept rate of 0.001.

While the identification process is as effortless as taking a photograph, according to Kakadiaris, there is a fairly complex and expensive technology behind the approach: Using a 3D as well as a infrared camera, a 3D mesh is overlaid with texture, infrared and, in future, time data. The captured data represents the biometric signature of an individual. Both in verification and identification processes, data captured in real time can be compared against available data in a database containing signatures. According to Kakadiaris, URxD already has shown its capabilities in government testing and he believes that the technology could be used for everything from gaining access to secure facilities to authorizing credit card purchases.




Merging mesh and texture data with an infrared image.


A key technology where CBL’s approach is different from current technologies is the way how the infrared camera is used. Kakadiaris told us that the results gained from this device help the technology to sort out foreign objects such as glasses, hats or band-aids. “To fool the system, you will need to undergo plastic surgery,” he said.

At this time, the system demonstrated is a high-end solution that is quite expensive and cannot make it into consumer applications as a result. The 3D image sensor required for URxD alone carries a price tag north of $20,000. However, Kakadiaris believes that his technology is suited for consumer applications and could be adopted in coming years when 3D sensors become more affordable and are scaled down to the size of a microphone chip. Processing power available in today’s computers is already sufficient for the system: According to the scientist, the data processing necessary to identify a face is done “in a matter of seconds.”






Acsys Veraport uses advanced face tracking and recognition technology to provide the ultimate in non-intrusive biometric access control. Acsys Veraport provides a complete, user-friendly access control solution for small- to medium-scale facilities - up to 200 access points and 20,000 users.  Acsys Veraport’s two-tier, client-server architecture can be inserted into existing access controls systems, and it can also be integrated with third party time & attendance solutions.


Better Face-Recognition Software

Computers outperform humans at recognizing faces in recent tests.

By Mark Williams

smaller text tool icon medium text tool icon larger text tool icon
Face facts: The top 3-D image only shows the information associated with the shape of a man’s face. The lower image shows the texture as well as the shape.
Credit: FRVT 2006 and ICE 2006 Large-Scale Results

For scientists and engineers involved with face-recognition technology,the recently released results of the Face Recognition Grand Challenge--more fully, the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006--have been a quiet triumph. Sponsored by the National Institute of Standards and Technology (NIST), the match up of face-recognition algorithms showed that machine recognition of human individuals has improved tenfold since 2002 and a hundredfold since 1995. Indeed, the best face-recognition algorithms now perform more accurately than most humans can manage. Overall, facial-recognition technology is advancing rapidly.

Jonathon Phillips, program manager for the NIST tests and lead author of the agency's report, says that the intended goal of the Face Recognition Grand Challenge was always an order-of-magnitude improvement in recognition performance over the results from 2002. Phillips believes that the necessary decrease in error rate to achieve that goal was due in large measure to the development of high-resolution still-images and 3-D face-recognition algorithms. "For the FRVT 2006 and the ICE 2006, sets of high-resolution face images, 3-D face scans, and iris images were collected of the same people," Phillips says. "The FRVT 2006 for the first time measured the performance of six 3-D algorithms on a set of 3-D face scans. The ICE 2006 measured the performance of ten algorithms on a set of iris images. 3-D face recognition has come into its own in the last few years because 3-D sensors for face recognition have become available only recently. What 3-D face recognition contributes is that it directly captures information about the shapes of faces."

Among other advantages, 3-D facial recognition identifies individuals by exploiting distinctive features of a human face's surface--for instance, the curves of the eye sockets, nose, and chin, which are where tissue and bone are most apparent and which don't change over time. Furthermore, Phillips says, "changes in illumination have adversely affected face-recognition performance from still images. But the shape of a face isn't affected by changes in illumination." Hence, 3-D face recognition might even be used in near-dark conditions.

According to Ralph Gross, a researcher at the Carnegie Mellon Robotics Institute, in Pittsburgh, 3-D facial recognition can also recognize subjects at different view angles up to 90 degrees--in other words, faces in profile. "Face recognition has been getting pretty good at full frontal faces and 20 degrees off, but as soon as you go towards profile, there've been problems." Gross says that the explanation for face-recognition software's difficulties with profiles may be no more complicated than the fact that no one was focusing on the problem. The main applications of face recognition have been in contexts like ID cards and face scanners, for which the aim has been recognition of the full frontal faces of cooperative subjects under controlled lighting

The Acsys FRS SDK allows third-party developers to implement our advanced facial biometric technology within their own security applications. The SDK provides tracking, enrollment, verification, classification, database, communication, and multimedia controls. It is also accompanied by demo applications with sample enrollment, verification, and classification application source code written in Visual C++.

The SDK uses Distributed Component Object Model (DCOM) technology to provide real-time face tracking, and face recognition that can be implemented in a stand-alone configuration, or within 3-tier architectures (FRS client/FRS server/central database). The SDK also provides support for LAN, WAN or Web-based communication between the FRS server, the central database server, and up to 4000 FRS clients.



High-resolution still images have been another factor in the improvement of face-recognition technology, in part because highly detailed skin-texture analysis has also become possible. With such analysis, any patch of skin--called a skin print--can be captured as an image, then broken up into smaller blocks that algorithms turn into mathematical, measurable spaces in which lines, pores, and the actual skin texture are recorded. "It can identify differences between identical twins, which isn't yet possible using facial-recognition software alone," Gross explains. "By combining facial recognition with surface-texture analysis, accurate identification can increase by 20 to 25 percent."

What about the FRVT report's claim that some face-recognition algorithms equal or exceed humans' recognition capabilities? Phillips explains: "Humans are very good at recognizing faces of familiar people. However, they aren't so good at recognizing unfamiliar people." Since many proposed face-recognition systems would complement or replace humans, the FRVT's comparative tests of the face-recognition capabilities of humans and software--the first such testing--were important for measuring the potential effectiveness of applications. Phillips says that at low false accept rates (a false accept rate is the measure of the likelihood that a biometric security system will incorrectly accept an access attempt by an unauthorized individual), six out of seven automatic face-recognition algorithms were comparable to or better than human recognition. These were algorithms from Neven Vision, Viisage, Cognitec, Identix, Samsung Advanced Institute for Technology, and Tsinghua University. Unfortunately, Phillips adds, "because the majority of FRVT 2006 participants haven't disclosed the details of their methods, it's not possible yet to assess what's distinctive about these algorithms."

How does the commercial payoff for face recognition look? Quite promising, because dozens of companies aim to cash in on face recognition's potential as a biometric for credentialing and verification purposes. For the FRVT, venerable corporations like Toshiba and Samsung competed alongside companies like Neven Vision--just acquired by Google--and Viisage and Identix (which have just merged into L1 Identity Solutions), as well as alongside researchers from universities as diverse as Beijing, Cambridge, and Carnegie Mellon. What applications does a company like Google foresee for the technology developed by its recent acquisition, Neven Vision? According to a Google PR person, "We believe it offers promising integration possibilities with Google's services, such as Picasa and Picasa Web Albums, particularly in terms of helping users organize and search their own photos."

At Carnegie Mellon, Ralph Gross says that among other efforts, he and his colleagues have been "involved with local DMVs in order to scan images for driver's licenses. I've gotten reports from the state level to say that, using face-recognition technology, they caught quite a number of people who applied for licenses in either different states or in the same state under a different name because their previous license got suspended." It's a growing trend. States using such technology include Massachusetts, Illinois, West Virginia, Wisconsin, Colorado, North and Southern Carolina, Oklahoma, North Dakota, Arkansas, and Mississippi. Nevertheless, Gross stresses, applying face-recognition technology to ID photos is a long way from having the capability that would let law enforcement search a city's webcam networks for specific individuals. "With driver's license photos, you have a controlled background, an operator telling you exactly how to position your face; the images are collected under comparable conditions. It's much more restricted than the random-face-in-the-crowd problem, where you're sticking a camera on a building."


The ability to control access to and prevent the theft of sensitive documents is crucial.  I-Cube, in conjunction with AFRIGROUP will provide biometric facial verification, know as VeraShield, which is  designed as a logical access control solution to work with all Windows ® network configurations, including single computer, Workgroups, single Domain controller, and multi-Domain networks. The Acsys VeraShield system extends the standard Windows ® security interface layer at the operating system level by providing biometric authentication using facial recognition on top of the existing password protection.

 On any login or unlock attempt to a computer with VeraShield, the User’s identity is verified using face recognition. In compliance with standard Windows operating procedures, the Acsys VeraShield system allows a user to logon to any computer within the network domain, using biometric authentication. The only noticeable difference to the user is the live video embedded within the standard logon screens.

To the User, VeraShield is non-intrusive and does and not require any additional steps during the logon operation. The User performs the normal Logon procedure, entering Username and Password, however at the end of the session, the User looks towards the camera. If the User’s identity is verified the Login or unlock procedure will proceed, otherwise the Login or unlock operation is denied.

 In addition, the I-CUBE VeraShield system provides several optional features for continuous security monitoring (CSM) while the user is logged on to the network, including:

· Shutdown following failure of facial authentication (customizable by period and number of authentication attempts)

· Immediate shutdown following user absence

· Shutdown if the current logged in User is not detected

· Shutdown on detection of multiple individuals (faces)

· Shutdown on detection of a denied user (denied user may be anyone registered within a domain)

The VeraShield system is integrated with the Windows ® Event Viewer which contains robust database editing facilities and associated event logging so that all biometric security operations pertaining to VeraShield triggered shutdown events are logged and retrievable by time/date, user, computer and event type.


Technical explanation of the HNeT technology


Still, Gross says, "you can already see the path building." Until recently, the video-surveillance industry still mostly relied on analog cameras, requiring cable to be set up for long distances to connect those cameras to monitoring equipment. Now, "the industry is switching to IP-based cameras, with which you can pretty easily tap into already existing Ethernet networks," Gross says. "So you have wireless cameras and cameras using POE [Power over Ethernet technology allows IP telephones, wireless LAN Access Points, and other appliances to receive power as well as data over existing LAN cabling] where you don't need a separate power plug. You can buy commercial solutions that are essentially a TiVo for these cameras, with motion sensors built in so they only record when there's motion happening. With digital storage, you can keep the data indefinitely and enhance it in ways that you can't with analog images. So all these things are coming together."

In principle, therefore, as face-recognition software continues its rapid advance, it will likely be possible to search for specific faces across a network of webcams. Accordingly, Gross's recent work at Carnegie Mellon, in conjunction with colleagues at the Data Privacy Lab there, has been the development of algorithms to protect individuals' privacy while under video surveillance. The usual methods that thwart human recognition of an individual's features on video--for example, those pixelated fields sometimes covering faces and body parts on reality-TV shows--already won't fool much face-recognition software. Completely blacking out each face in a video clip would do the job, but this would be of limited use if law-enforcement agencies wanted to follow up evidence of suspicious behavior once they had a court warrant. The function of the privacy-preserving algorithms that Gross is helping to create, he explains, is to automatically take the average values of individuals' faces and, from those, synthesize new facial images, then superimpose those new images over the originals. "It may seem like the opposite technology," Gross says, "but actually, it's just the other side of face recognition."




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