Face detection and recognition using neural network and hidden Markov models

This paper proposes an integrated system for face recognition in cluttered images. An extension of the system is able to recognize faces by directly working with JPEG compressed image data without decompressing. The system consists of face detection using a polynomial neural network (PNN) and face recognition using pseudo-2D hidden markov models (P2D HMM or pseudo-2D HMM). Firstly, images from a database or a website are rescaled and preprocessed to form a standard size and file format. Secondly, face regions in image are located and extracted by PNN. Finally through face recognition the detected faces are labeled as one person of the database or rejected as "new subject". Our experiments show that the face detector has a higher detection rate and low false positive rate. At the same time, the face recognition achieves higher performance with lower complexity and less computation time compared with other systems.