Face recognition system using DCT features implemented on DSP processor

Face recognition is a challenge because the faces always change due to facial expression, direction, light, and scale. Furthermore, it needs good computing techniques for recognition in order to reduce the system’s complexity. Our approach focuses on the local feature extraction in the frequency domain. DCT was proposed as the feature extraction algorithm for face recognition, which captures the important features in the face image and at the same time reduces the feature space. PCA then performs the feature reduction of the extracted image and produces a small size of feature vector. The propose method can reduce data dimension in feature space. The classification is done by using the Euclidean distance between the projection test and projection train images. The algorithm is tested using DSP processor and achieve a same performance with PC based. The extensive experimentations that have been carried out upon standard face databases such as ORL shows that significant performance is achieved by this method, which is 98.5% for best selected test image and 95% for the worst selected test image. Besides that, execution time is also measured, whereby to recognize 40 people, the system only requires 0.3313 second. The proposed method not only offers computational savings, but is also fast and has a high degree of recognition accuracy.