An Architecture for Real Time Face Recognition using WMPCA
A. Pavan Kumar, Veezhinathan Kamakoti, Sukhendu Das · Indian Conference on Computer Vision, Graphics and Image Processing · 2004
An architecture for real time face recognition using weighted modular principle component analysis (WMPCA) is presented in this paper. The WMPCA methodology splits the test face horizontally into sub-regions and analyzes each sub-region separately using PCA. The final decision is taken based on a weighted sum of the errors obtained from each region. This is based on assumption that different regions in a face vary at different rates with variations in expression and illumination. The WMPCA methodology has a better recognition rate, when compared with conventional PCA, for faces with large variations in expression and illumination. This methodology has a wide scope for parallelism. An architecture which exploits this parallelism is proposed in this paper. We also present a System On Programmable Chip (SOPC) implementation of face recognition system using this architecture.