Gray Scale Image Recognition using Finite State Automata

In image processing, processed images of faces can be seen as vectors whose components are the brightness of each pixel. The dimension of this vector space is the number of pixels. The eigenvectors of the covariance matrix associated with a large set of normalized pictures of faces are called eigen faces. They are very useful for expressing any face image as a linear combination of some of them. In the facial recognition branch of biometrics, eigen faces provide a means of applying data compression to faces for identification purposes. Research related to eigen vision systems determining hand gestures has also been made This paper is about human face recognition using finite automata. Face recognition involves matching a given image with the database of images and identifying the image that it resembles the most. In this paper, face recognition is achieved using IMED (Image Euclidean Distance) and Frechet distance and tested using standard database. Euclidean distance uses the prior knowledge that pixels located near one another have little variance in gray levels, and determines the relationship between pixels only according to the distance between pixels on the image lattice. In many applications, however, we are only interested in face images. Therefore, more prior knowledge can be obtained from these images to determine the relationship between pixels.