IMAGE PROCESSING FACE RECOGNITION USING EIGENFACES

IMAGE PROCESSING FACE RECOGNITION USING  EIGENFACES

ABSTRACT

        Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and lighting conditions.
       It is a near-real-time computer system that can identify an unknown subject’s face and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy.
       The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as Eigenfaces because they are the eigenvectors of the set of faces. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner.   

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