Face Recognition by the LDA-Based Algorithm for a Video Surveillance System on DSP

Abstract

Face recognition is an important part of today’s emerging biometrics and video surveillance markets. As face recognition algorithms move from research labs to real world product, power consumption and cost become critical issues, and DSP-based implementations become more attractive. Also, “real-time” automatic personal identification system should meet the conflicting dual requirements of accuracy and response time. In addition, it also should be user-friendly. This paper proposes a method of face recognition by the LDA (Linear Discriminant Analysis) Algorithm with the facial feature extracted by chrominance component in color images. We designed a face recognition system based on a DSP (Digital Signal Processor). At first, we apply a lighting compensation algorithm with contrast-limited adaptive histogram equalization to the input image according to the variation of light condition. While we project the face image from the original vector space to a face subspace via PCA (Principal Component Analysis), we use the LDA to obtain the best linear classifier. And then, we estimate the Euclidian distances between the input image’s feature vector and trained image’s feature vector. The experimental results with real-time input video show that the algorithm has a pretty good performance on DSP-based face recognition system.