Face-matching method using deep convolution discrimination network
Aiming at the problems of high scene complexity,illumination,and occlusion in face matching in practi-cal applications,this paper proposes the face-matching algorithm MTC-FaceNetSDM based on a deep convolution discrimination network to improve the accuracy of face matching.First,the deep convolutional neural network framework in MTC-FaceNetSDM was established,and the MTC-FaceNet network was obtained by embedding a mul-titask cascaded convolutional neural network in the front of the FaceNet network structure.Then,the deep convolu-tional neural network was used to obtain high-dimensional face depth features,and the Euclidean distance module in the FaceNet network structure was replaced with the proposed similarity discrimination module(SDM)for high-dimensional face feature vector matching.Finally,the self-made face datasets C-facev1 and CASIA-WebFace were used to train the face-matching algorithm proposed in this paper,and the face datasets LFW and CASIA-FaceV5 were used to evaluate the performance of the trained model.The experimental results showed that the face-matching accuracy of MTC-FaceNetSDM was 1.48%higher than that of MTC-FaceNet.Moreover,the Chinese face-matching accuracy was increased by 3.80%,thus showing the proposed algorithm's capability for multiethnic face matching.Moreover,the proposed algorithm had favorable robustness and generalization ability,achieving excellent face com-parison results,which could be practically applied to face verification systems.
万方数据
维普
