采用局部二值模式与深度信念网络的人脸识别

针对人脸维度过高和人脸局部特征提取易忽略的问题，提出了一种将多尺度局部二值模式( LBP)算法与深度信念网络( DBN)算法相结合的人脸识别方法。首先采用多尺度LBP算法提取人脸纹理特征，进而将LBP提取的纹理特征作为深度信念网络的输入，最后通过逐层网络训练，得到网络的最优参数，并在ORL人脸库中进行测试，识别率可达95.2%，比使用Gabor小波和主成分分析(PCA)算法的人脸识别高2.6%，说明该算法具有很好的人脸识别能力。

Facial recognition using local binary pattern and deep belief network

In view of the large dimensions of facial features and the neglect of the local features extraction, this paper proposes a facial recognition method based on multi-scale local binary pattern( LBP) and deep belief network( DBN) algorithm. Firstly,the texture features are extracted by using multi-scale LBP,then these features are inputed to DBN,and finally the optimal parameters of the network are obtained by layered network training. A test on the ORL faces database is performed and recognition rate reaches 95. 2%, 2. 6% higher than that of face recognition using Gabor wavelet and pincipal component analysis(PCA) al-gorithms,which illustrates that the proposed algorithm has a good face recognition ability.