In today's networked world, the need to maintain the security of information or physical property is becoming both increasingly important and increasingly difficult. From time to time we hear about the crimes of credit card fraud, computer break-in's by hackers, or security breaches in a company or government building. In the year 1998, sophisticated cyber crooks caused well over US $100 million in losses (Reuters, 1999). In most of these crimes, the criminals were taking advantage of a fundamental flaw in the conventional access control systems: the systems do not grant access by "who we are", but by "what we have", such as ID cards, keys, passwords, PIN numbers, or mother's maiden name. None of these means are really define us. Rather, they merely are means to authenticate us. It goes without saying that if someone steals, duplicates, or acquires these identity means, he or she will be able to access our data or our personal property any time they want. Recently, technology became available to allow verification of "true" individual identity. This technology is based in a field called "biometrics". Biometric access control are automated methods of verifying or recognizing the identity of a living person on the basis of some physiological characteristics, such as fingerprints or facial features, or some aspects of the person's behavior, like his/her handwriting style or keystroke patterns. Since biometric systems identify a person by biological characteristics, they are difficult to forge.
Among the various biometric ID methods, the physiological methods (fingerprint, face, DNA) are more stable than methods in behavioral category (keystroke, voice print). The reason is that physiological features are often non-alterable except by severe injury. The behavioral patterns, on the other hand, may fluctuate due to stress, fatigue, or illness. However, behavioral IDs have the advantage of being non-intrusiveness. People are more comfortable signing their names or speaking to a microphone than placing their eyes before a scanner or giving a drop of blood for DNA sequencing.
Face recognition is one of the few biometric methods that possess the merits of both high accuracy and low intrusiveness. It has the accuracy of a physiological approach without being intrusive. For this reason, since the early 70's (Kelly, 1970), face recognition has drawn the attention of researchers in fields from security, psychology, and image processing, to computer vision. Numerous algorithms have been proposed for face recognition; for detailed survey please see Chellappa (1995) and Zhang (1997).
While network security and access control are it most widely discussed applications, face recognition has also proven useful in other multimedia information processing areas. Chan et al. (1998) use face recognition techniques to browse video database to find out shots of particular people. Li et al. (1993) code the face images with a compact parameterized facial model for low-bandwidth communication applications such as videophone and teleconferencing.
Recently, as the technology has matured, commercial products (such as Miros' TrueFace (1999) and Visionics' FaceIt (1999)) have appeared on the market. Despite the commercial success of those face recognition products, a few research issues remain to be explored. In the next section, we will begin our study of face recognition by discussing several metrics to evaluate the recognition performance. Section 3 provides a framework for a generic face recognition algorithm. Then in Section 4 we discuss the various factors that affect the performance of the face recognition system. In section 5, we show the readers several famous face recognition examples, such as eigenface and neural network. Then finally a conclusion is given in section 6.
Performance Evaluation Metrics
The two standard biometric measures to indicate the identifying power are False Rejection Rate (FRR) and False Acceptance Rate (FAR). …