Abstract: Computing performance for massive face data is one of the key problems for face recognition on surveillance device.To improve the performance of embedded face recognition systems,a novel parallel feed forward neural network acceleration framework was established based on CPU-multicore accelerator heterogeneous architecture firstly.Secondly,a feature extraction method based on PCA algorithm was used to extract face features for neural network training and classification.Thirdly,the trained neural network parameters can be imported to the parallel neural network framework for face recognition.Finally,the architecture was implemented on hardware platform named Parallella integrating Zynq Soc and Epiphany.The experimental results show that the proposed implementation obtains very consistent accuracy than that of the dual-core ARM,and achieves 8 times speedup than that of the dual-core ARM.Experiment results prove that the proposed system has significant advantages on computing performance.

Key words: Face recognition,Multicore processor,Neural network,Primary component analysis,Parallella