System level design of real time face recognition architecture based on composite PCA

Abstract

Design and implementation of a fast parallel architecture based on an improved principal component analysis (PCA) method called Composite PCA suitable for real-time face recognition is presented in this paper. The proposed architecture performs the tasks of both feature extraction and classification. Composite PCA takes in to consideration the local features of face images, which do not vary widely between face images of the same person taken under varying expression, illumination and pose. Hence it leads to a better recognition rate than PCA. Composite PCA has more parallelism than conventional PCA and this parallelism is utilized to design an efficient architecture capable of performing real-time face recognition. The face recognition system is implemented in an FPGA environment and tested using standard databases. The system is able to recognize a person from a database of 110 images of 10 individuals in approximately 4 ms.