关键词: 人脸验证, 生成式对抗网络（GAN）, 深度特征迁移, 人脸模拟, 跨年龄识别

Abstract:

Aiming at the problem of the effect of age change on the face recognition rate, a cross-age face generation method DFM-GAN（Depth Feature Migration GAN） is proposed by combining Generative Adversarial Network（GAN） and deep feature migration, and cross-age experimental research on simulated face verification is carried out. Firstly, the real sample is mapped to the feature vector by a convolutional encoder, and then the vector is projected to the face set under the one-hot coding age condition by using a deconvolution generator. By migrating the database sample face texture style, semantic features and other attributes simulate the facial images of people at different ages to be tested, reducing the differences with database samples. At the same time, the sample data set is pre-processed by Gaussian edge blurring method, and the edge enhancement counter-loss function is introduced to make the generated image have clearer edges. Local color histogram matching is performed on the generated image to increase the contrast and increase the face recognition performance of cross-age person. A single-sample experiment at different ages and a multi-sample experiment at specified ages are carried out. Experimental measurements are performed using two indicators of face similarity and face distance. The results show that the cross-age data samples generated by DFM-GAN face images are similar. The average degree has been increased by 19.24 percentage points, and the average face distance has been reduced by 0.451, which has better feasibility and certain practical significance in the direction of face recognition across ages.

Key words: face verification, Generative Adversarial Network（GAN）, depth feature migration, face simulation, cross-age recognition