Rapid Face Identification Technology

Currently there are many methods of biometric identification: fingerprint, eye iris, retina, voice, face etc. Each of these methods has certain advantages and disadvantages, which must be considered in biometrical system developing: system reliability, price, flexibility, necessity of physical contact with scanning device and many others. Selecting the certain biometrical identification method or using the multi-biometrical system can help to support these, often discrepant, requirements. 

Face identification can be an important alternative for selecting and developing optimal biometrical system. Its advantage is that it does not require physical contact with image capture device (camera). Face identification system does not require any advanced hardware, it can be used with existing image capture devices (web cams, security cameras etc.). 

Face is not so unique as fingerprints and eye iris, so its recognition reliability is slightly lower. However, it is still suitable for many applications, taking into account its convenience for user. It can also be used together with fingerprint identification or another biometrical method for developing more security critical applications. 

Multi-biometrical approach is especially important for identification (1:N) systems. Identification systems are very convenient to use because they do not require any additional security information (smart cards, passwords etc.). On the other hand, 1:N-matching routine usually accumulates False Acceptance probability, which may become unacceptable big for applications with large databases. Using face identification as additional biometrics can dramatically decrease this effect. Multi-biometrical approach also usually helps in situations where certain biometric feature is not optimal for special customers groups. For example, hard workers may have raw fingerprints, which may increase false rejection rate if fingerprint identification was used alone. 

Thus, face identification should be considered as a serious alternative in biometrical or multi-biometrical systems developing. 

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Why VeriLook?

Click to show multiple faces detection Neurotechnologija has developed a PC-based face recognition algorithm VeriLook 3.1 designed for biometrical system integrators. VeriLook 3.1 offers capabilities of the most advanced and convenient facial identification systems at a reasonable cost: Reliability. VeriLook 3.1 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 3.1 face enrollment time is less than 0.3 sec and matching speed is 100,000 faces per second in 1:N identification mode. Multiple face processing. VeriLook 3.1 detects all faces in the current frame and allows to process all of them simultaneously. VeriLook doesn't require any specific hardware. Face image can be obtained from a webcam or other low cost camera. Image processing and recognition are performed on ordinary PC. Both face and fingerprint recognition technologies from the same vendor. Compatible product interfaces and customer support from the same source allow simple multi-biometric system integration and helps to achieve high system recognition quality. VeriLook algorithm can be used alone or together with other Neurotechnologija biometrical algorithms. VeriLook is designed not only for verification, but also for identification (1:N matching).

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Algorithm

VeriLook 3.1 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 still images as well as in live video streams. 
• Simultaneous multiple face processing and identification in single frame. All faces on the current frame are detected in less than 0.1 seconds and then each face is processed in less than 0.13 seconds.*
• VeriLook 3.1 face template matching algorithm compares up to 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.

Note: All performance evaluations were performed using PC with 3 Ghz Intel Pentium4 CPU. 

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Reliability Tests

Click to enlarge

VeriLook 3.1 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 operating curves (ROC), which show the dependence of false rejection rate on the false acceptance rate. The presented ROC shows the results of testing VeriLook 3.0 with face images from XM2VTSDB database. The red curve shows the performance results of VeriLook 3.1 without generalization, and the green one shows the results of VeriLook 3.1 with generalization. VeriLook 3.1 algorithm technical specifications Recommended minimal image size 640 x 480 pixels Multiple faces detection time (using 640 x 480 image) 0.07 sec. Single face processing time (after detecting all faces) 0.13 sec. Matching speed 100,000 faces/sec. Size of one record in the database 2.3 Kbytes Maximum database size unlimited All performance evaluations were performed using a PC with 2.8 GHz Intel Pentium4 CPU

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System Requirements

PC with Pentium 1GHz processor; 
128 Mb of RAM; 

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Related products: 

These products are based on VeriLook 3.0 algorithm: 

• VeriLook 3.1 Standard SDK

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