An efficient face recognition algorithm based on a modular PCA approach that has an improved recognition rate for large variations in pose, lighting direction and facial expression is being developed in the VLSI Systems Laboratory. In this technique the face images are divided into smaller sub-images and the PCA approach is applied to each of these sub-images. The training phase of the technique begins by extracting the eigenvectors corresponding to the largest eigenvalues of a covariance matrix, which is constructed from the training image database. These eigenvectors are used for creating a generalized face feature vector, which can classify the incoming face images successfully. Experimental results demonstrate that the modular PCA method has higher recognition rate when compared with the traditional PCA method for tests conducted on the UMIST and Yale databases.

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