基于深度神经网络的特征加权融合人脸识别方法

针对目前难以提取到适合用于分类的人脸特征以及在非限条件下进行人脸识别准确率低的问题,提出了一种基于深度神经网络的特征加权融合人脸识别方法(DLWF)。首先,应用主动形状模型(ASM)提取出人脸面部的主要特征点,并根据主要特征点对人脸不同器官区域进行采样;然后,将所得采样块分别输入到对应的深度信念网络(DBN)中进行训练,获得网络最优参数;最后,利用Softmax回归求出各个区域的相似度向量,将多区域的相似度向量加权融合得到综合相似度评分进行人脸识别。经ORL和WFL人脸库上进行实验验证,DLWF算法的识别准确率分别达到97%和88.76%,与传统算法主成分分析(PCA)、支持向量机(SVM)、DBN及FIP+线性判别式分析(LDA)相比,无论是限制条件还是非限制条件下,识别率均有提高。实验结果表明,该算法具有高效的人脸识别能力。

Face recognition based on deep neural network and weighted fusion of face features

It is difficult to extract suitable face feature for classification, and the face recognition accuracy is low under unconstrained condition. To solve the above problems, a new method based on deep neural network and weighted fusion of face features, namely DLWF, was proposed. First, facial feature points were located by using Active Shape Model (ASM), then different organs of face were sampled according to those facial feature points. The corresponding Deep Belief Network (DBN) was trained by the regional samples to get optimal network parameters. Finally, the similarity vector of different organs was obtained by using Softmax regression. The weighted fusion of multiple regions in the similarity vector method was used for face recognition. The recognition accuracy got to 97% and 88.76% respectively on the ORL and LFW face database; compared with the traditional recognition algorithm including Principal Components Analysis (PCA), Support Vector Machine (SVM), DBN, and Face Identity-Preserving (FIP) + Linear Discriminant Analysis (LDA), no matter under the constrained condition or the unconstrained condition, recognition rates were both improved. The experimental results show that the proposed algorithm has high efficiency in face recognition.