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.
Neurotechnologija has developed a face identification algorithm VeriLook 2.0 and Software Development Kit, which are designed for biometric system integrators. VeriLook 2.0 offers capabilities of the most advanced and convenient face identification systems at a reasonable cost:
VeriLook 2.0 face recognition algorithm implements advanced face localization, enrollment and matching using robust digital image processing algorithms:
Note: All performance evaluations were performed using PC with 3 Ghz Intel Pentium4 CPU.
These products are based on VeriLook 2.0 algorithm: