Dimensionality Reduction Techniques for Face Recognition

Abstract

High level of image content analysis is required for several applications. This is taking more significance as the number of digital images stored is growing exponentially. On the one hand the technology should help store these images, on the other, enable us to develop newer algorithmic models aimed at efficient and quick retrieval of images. The entire captured data may not be applicable for an application and hence deriving a subset of data to achieve objective function is desirable. Face detection and recognition are preliminary steps to a wide range of applications such as personal identity verification, video-surveillance, facial expression extraction, gender classification, advanced human and computer interaction. A face recognition system would allow user to be identified by simply walking past a surveillance camera. Research has been devoted to facial recognition for years and has brought forward algorithms in an attempt to be as accurate as humans are. A face recognition system is expected to identify faces present in images and videos automatically. It can operate in either or both of two modes: