Face recognition using Parallel Associative Memory

This paper proposes a parallel associative memory (PAM) based face recognition system. An image is split into blocks which are processed in parallel by conventional associative memory. The combined output of these are used to discriminate between faces. Further, a parameter termed as run length count has been used to discriminate between similar faces. This approach helps to scale up associative memory to higher resolution images with more detailed features thus improving recognition performance. To analyze the efficiency and goodness of the proposed system the experiments have been performed on ORL face database. The proposed method out performed most of the existing methods.