Development of face recognition system based on PCA and LBP for intelligent anti-theft doors

The face recognition system of intelligent anti-theft door by the embedded processor S3C6410 platform drive USB camera to capture the face data, it uses AdaBoost algorithm for detecting and classifying face region gradually in Opencv face database. And then, it uses Local Binary Pattern (LBP) operator with LBP image coding to describe the texture feature of local area which can extract facial feature rapidly. In the end, Principal Component Analysis (PCA) method is used for reducing facial feature matrix dimensionality, which reduces the amount of calculation and data quantity and improves the recognition speed greatly at the same time. The unlock part of the Anti-theft door reads data to unlock or alarm. After MATLAB simulation, the system is transplanted to the embedded device, and the results show that the system is stable, fast and efficient, and has a good commercial value.