Abstract

Since last few years, face Recognition has become one of the most challenging task in the pattern recognition field. The Face recognition plays very important role in many applications like video surveillance, retrieval of an identity from a database for criminal investigations and forensic applications. The face is considered as good biometric for many reasons: the acquisition process is nonintrusive and does not require collaboration of the subject to be recognized. The acquisition process of a face from a scene is simpler and cheaper than the acquisition of other biometrics as the iris and the fingerprint. On the other hand, many problems arise, because of the variability of many parameters like face expression, pose, scale, lighting, and other environmental parameters.Face recognition involved in application like problem of recognition of an identity in a scene. A system that automatically recognizes a face in a scene, first detects it and normalize it with respect to the pose, lighting and scale. Then, the system tries to associate the face to one or more faces stored in its database, and gives the set of faces that are considered as nearest to the detected face. This requires more computational resources and very robust algorithms for detection, normalization and recognition. In this paper we have implement different face recognition methods like Principle component analysis, Linear discriminant analysis and Fusion of PCA and LDA for face recognition. And better recognition rate is achieved by implementing different similarity measures between images.

Keywords

References

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