VeriLook Face Recognition Technology

Content of this page

• Why VeriLook?
• Algorithm
• Reliability Tests and Technical Specifications
• Algorithm's Demo
• Related Products

Why VeriLook?

Neurotechnology has developed a PC-based face recognition algorithm VeriLook 3.2 designed for biometrical system integrators. VeriLook 3.2 offers capabilities of advanced and convenient facial identification systems at a reasonable cost:

• Reliability. The VeriLook 3.2 algorithm has been tested with standard face databases (FERET, XM2VTSDB and others). The results are one of the best among existing face identification systems on the market.
• Speed. VeriLook 3.2 face enrollment time is less than 0.3 sec. and matching speed is 100,000 faces per second in 1:N identification mode. To confirm these results with your samples, please try VeriLook algorithm demo application.
• Live face detection. A conventional face identification system can be easily cheated by placing a photo of another person in front of a camera. VeriLook is able to prevent this kind of security breach by determining whether a face in a video stream belongs to a real human or is a photo.
• Multiple Face Detection
  
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  Multiple face processing. VeriLook 3.2 detects all faces in the current frame and can process all of them simultaneously.
• VeriLook doesn't require any specific hardware. The face image can be obtained from a webcam or other low cost camera. Image processing and recognition are performed on an ordinary PC.
• Biometrical template record can contain multiple face samples belonging to the same person. These samples can be enrolled with different face postures and expressions, from different sources and in different time thus allowing to improve matching quality. For example a person could be enrolled with and without eyeglasses or with different eyeglasses, with and without beard or moustache, smiling and non-smiling etc.
• Fingerprint, iris and face recognition technologies from the same vendor. Compatible product interfaces and customer support from the same source allow simple multi-biometric system integration and help to achieve high system recognition quality. The VeriLook algorithm can be used alone or together with other Neurotechnology's biometrical algorithms.
• VeriLook is designed not only for verification, but also for identification (1:N matching).

Algorithm

The VeriLook 3.2 face recognition algorithm implements advanced face localization, enrollment and matching using robust digital image processing algorithms:

• Fast and accurate face localization for reliable detection of multiple faces in live video streams and still images.
• Simultaneous multiple face processing and identification in single frame. All faces on the current frame are detected in 0.07 sec.^* and then each face is processed in 0.13 sec.^*.
• Face quality threshold can be used during face enrollment to ensure that only the best quality face template will be stored into database.
• The VeriLook 3.2 face template matching algorithm compares 100,000 faces per second^*.
• Applications implemented using VeriLook SDK can handle large face databases, as one face features template is only 2.3 Kbytes.
• Features generalization mode generates the collection of the generalized face features from several images of the same subject. Then, each face image is processed, features are extracted, and the collections of features are analyzed and combined into a single generalized features collection, which is written to the database. This way, the enrolled feature template is more reliable and the face recognition quality increases considerably.
• VeriLook has certain tolerance to face posture that assures face enrollment convenience: rotation of a head can be up to 10 degrees from frontal in each direction (nodded up/down, rotated left/right, tilted left/right).
• Biometrical template record can contain several face samples belonging to the same person. These samples can be enrolled from different sources and in different time thus allowing to improve matching quality. For example a person could be enrolled with and without eyeglasses or with different eyeglasses, with and without beard or moustache, etc.

^* All performance evaluations were performed using a PC with 2.8 GHz Intel Pentium4 CPU

Reliability Tests and Technical Specifications

VeriLook 3.2 was tested with face images from many cameras. The most interesting testing results are those obtained with standard databases, because in this case they can be compared with testing results of other algorithms. Usually the algorithm recognition quality is expressed by receiver operation characteristics (ROC) curves, which show the dependence of false rejection rate on the false acceptance rate. The presented ROC curves show the results of testing VeriLook 3.2 with face images from XM2VTSDB database. The red curve shows the performance results of VeriLook 3.2 without generalization, and the green one shows the results of VeriLook 3.2 with generalization.

Algorithm's Demo

The VeriLook demo application for Microsoft Windows 2000/XP/2003/Vista can be downloaded for evaluation of the VeriLook face recognition algorithm. The application enrolls and identifies faces from almost any camera or webcam, and image files. Internet connection is not required to run the application.

VeriLook 3.2 Standard SDK and Extended SDK trials are also available for downloading.

Related Products

These products are based on the VeriLook technology:

• VeriLook 3.2 Standard SDK;
• VeriLook 3.2 Extended SDK.