Face Recognition Techniques

Principle Component Analysis (PCA)

PCA, the technique introducing the concept of ‘Eigen faces’, converts two dimensional vectors into one dimensional vectors, that can be decomposed into orthogonal components (eigen faces). It extracts the features of face which vary the most from rest of the image, in this way insignificant data is discarded and what remains is the most effective low dimensional facial structure pattern. Now, each face image can be represented as weighted sum (feature vector) of eigen faces and can be stored in a one dimensional array. A probe image is then compared against a gallery image by measuring the distance between their respective feature vectors.

 

PCA is reasonably sensitive to scale variations and hence the probe and gallery image must be of same size and should be normalized to line up the features within the image. Also, the pose and illumination variation between the two images is not acceptable. 

 

Linear Discriminate Analysis:  

 

LDA is based on same statistical principles as PCA. It classifies faces of unknown individuals based on training sequence of known individuals. The technique maximizes the variation between classes (different individuals) and minimizes variations with in classes (samples of same person in varying pose, illumination.

 

The algorithm thus, discriminates between individuals and at the same time can recognize image of same individual with some minor variations of expression, rotation and lightning. 

Elastic Bunch Graph Matching (EBGM):

 

This technique is free from the limitations such as there should be no variations in illumination, pose angle, expression and contrast. The technique with its new approach considers non linear characteristics of face image such as pose variation (straight or leaned), lightning variations (outdoor or indoor) and expression variation. In this method, projection of face is made onto an elastic grid using a dynamic link architecture created by ‘Gabor wavelet transform’. A node on elastic grid, called as ‘Gabor jet’, describes the behaviour of image around the given pixel. Convolution of image with Gabor filter is done to extract features using image processing. The degree of similarity of Gabor filter response at each Gabor node is the basis of recognition. Accurate location of landmarks is must for this method.