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| Research on Unconstrained Face Recognition Based on DBNs Network |
| ZHAO Yi-zhong,LIU Wen-bo |
| College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China |
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Abstract To overcome the technical difficulties like high degrees of freedom and the complicated interference factors in unconstrained face recognition, an algorithm for unconstrained face recognition based on DBNs network is proposed with the adoption of deep learning theory. Based on relative entropy sparse restrictions and dropout mechanism, the optimization algorithm is designed. As for the problem of small sample in practice, an algorithm based on hybrid DBNs network model is proposed, which generates simulated samples with CNNs model to train the DBNs network. When tested by the standard face library, the experimental results show that the average recognition accuracy of DBNs and hybrid DBNs reach 97.0% and 90.3% respectively, which satisfy the practical using demand
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Received: 22 February 2016
Published: 28 December 2016
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2017, Vol. 38 
