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
赵一中,刘文波. 基于深度信念网络的非限制性人脸识别算法研究[J]. 计量学报, 2017, 38(1): 65-68.
ZHAO Yi-zhong,LIU Wen-bo. Research on Unconstrained Face Recognition Based on DBNs Network. Acta Metrologica Sinica, 2017, 38(1): 65-68.
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2017, Vol. 38 
