中文摘要：

随着互联网的发展，人脸识别在安全领域得到越来越广泛的应用．传统的人脸识别方法泛化能力较差，无法有效处理特别复杂的函数关系而使其在安全领域发展比较缓慢．为了提高人脸识别的正确率，本文提出了一种新的深度玻尔兹曼机神经网络（deep Boltzmann machine, DBM）和差异受限玻尔兹曼机神经网络（discriminative restricted Boltzmann machine, DRBM）的混合模型，在人脸数据集上与传统的人脸识别模型做了对比，为了进一步验证有效性，本文还选取在公共CMU—PIE人脸图像数据集上作了对比试验．实验发现：在两个数据集上，新的混合模型相对传统模型都有更好的识别效果，将产生直接的社会效益和管理意义，

英文摘要：

With the improvement of the Internet, face recognition has been widely adopted in the security area. Traditional face recognition methods have slowly development in the security area due to their poor generalization ability and inefficiency of dealing with the particularly complex function. In order to improve the accuracy of face recognition, this paper presents a new hybrid classifier model by combining deep Boltzmann machine （DBM） with discriminative restrict Boltzmann machine （DRBM） and compares with these traditional methods. This paper designs a sound experiment on the CMU-PIE face dataset to demonstrate the efficiency of the hybrid model. The results show that the hybrid classifier model has better recognition effect compared to the traditional methods on the two selected datasets, which would own more social benefits and help management decisions.