Abstract
Face recognition has been an active research area since late 1980s [1]. Eigenface approach is one of the earliest appearance-based face recognition methods, which was developed by M. Turk and A. Pentland [1] in 1991. In this approach we have to perform a lots of computations, which are not feasible with respect to time in many real time system. The concept of principal component analysis (PCA) is used in this approach to reduce the dimension and hence reducing the computation time. Principal component analysis [4] decomposes face images into a small set of characteristic feature images called eigen faces.
Highlights
Among various biological features, face plays an important role to uniquely identify a person
Step 4: we find which face image in the training set best matches the test face image
The first one is dynamic in nature and will show the test face and the recognized face
Summary
Face plays an important role to uniquely identify a person. In template-matching based technique, we represent the face as a template considering the relative distance between various facial features like distance between two eyes, distance between eye and nose, distance between nose and mouth. Instead of the whole face we can think each individual facial feature as a template like eye template, nose template and mouth template. We compare their property with the database image. Though these types of recognition methods are easy to implement but the memory requirement is very high. That is appearance-based approach, we project the face image onto a lower dimensional linear subspace. In this paper we will focus only on the appearance-based approach
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