中文摘要：

提出一种在非限制条件下,基于深度学习的人脸识别算法。同时,将LBP纹理特征作为深度网络的输入,通过逐层贪婪训练网络,获得良好的网络参数,并用训练好的网络对测试样本进行预测。在非限制条件下人脸库LFW上实验结果表明,该算法较传统算法（PCA、SVM、LBP）识别率高;另外,在Yale库和Yale-B库上也获得较高识别率,进一步说明以LBP纹理特征作为网络输入的深度学习方法能够对人脸图像进行准确识别。

英文摘要：

A face recognition method under unconstrained condition was proposed based on deep learning. At the same time, making LBP texture features as the input of deep learning net, and greedy training the network layer was made by layer to obtain good network parameters. At last, the trained net was used to predict the test samples＇ labels. The results of experiments on LFW（labeled faces in the wild） show that the algorithm can obtain higher recognition rate than traditional algorithms（such as PCA, SVM, LBP）.Otherwise, the recognition rate on Yale and Yale-B are also very high, the experi- mental results show that deep learning net with LBP texture as its input can classify face images correctly.