基于深度学习与特征融合的人脸识别算法

卷积神经网络在人脸识别研究上有较好的效果,但是其提取的人脸特征忽略了人脸的局部结构特征。针对此问题,文中提出一种基于深度学习与特征融合的人脸识别方法。该算法将局部二值模式信息与原图信息相结合作为SDFVGG网络的输入,使得提取的人脸特征更加丰富且更具表征能力。其中,SDFVGG网络是将VGG网络进行深浅特征相融合后的网络。在CAS-PEAL-R1人脸数据库上的实验表明,将网络深浅特征相融合与在卷积神经网络中加入LBP图像信息与原图信息相融合的特征信息对于提高人脸识别准确率非常有效,可得到优于传统算法和一般卷积神经网络的最高98.58%人脸识别率。

Face Recognition Algorithm Based on Deep Learning and Feature Fusion

The convolutional neural network performed well on face recognition, but the extracted facial feature ignores the local structure features of the face. In order to address this problem, a novel method was proposed, which was based on deep learning and feature fusion. This algorithm made the extracted facial features more comprehensive by using a combination of local binary pattern information and original image information as the input of SDFVGG network, which was a neuralnetwork fusing shallow and deep features of VGG network. Experimental results on the CAS-PEAL-R1 face database demonstrated that the proposed algorithm was very effective for improving the accuracy of face recognition, and achieved a maximum face recognition rate of 98.58% which was better than traditional algorithms and general convolutional neural networks.