Research on Parallelization of Face Recognition

Face recognition is the hotpot in the fields of biometrics recognition. Until now, the method of face recognition is very accurate and simple. The main way to improve the speed of face recognition is to improve the algorithm. This paper speeds the recognition system through partitioning the system with threads and grouping the data. It implements a face recognition system based on PCA algorithm and BP neural network, which parallelized using multi threads and SSE instructions. Firstly, preprocesses the original face image. Secondly, extracts the feature by PCA algorithm. The system is completed through training and identifying the face feature. Lastly, this paper partitions the system with multi threads, and groups the data to implement the parallelization. To parallelize the BP neural network is difficult, and the speed of convergence is slow. So, this paper gets a multi BP neural network by improving the BP neural network. The recognition rate of face recognition in this paper reaches 93.5%, which increases 5.2% compared to the system using PCA only and 2.1% compared to the system based on PCA and BP network. The speed of parallelized system using multi threads and SSE instructions is 5.984 times faster than serialized system.