面向非配合场景的人脸重建及识别方法

Face reconstruction and recognition in non-cooperative scenes

针对非配合场景下,姿态偏差及低分辨率条件下人脸重建效果差、识别率低等问题,提出一种三元对抗重建识别网络。该方法先通过编解码网络将人脸图像重建为高分辨率正脸图像,再对重建人脸进行识别。在网络训练上,设计了一种最短距离三元组损失函数,并将其与对抗机制结合,使网络对同一个人提取的特征相似性高,而与其他人提取出的特征相似度低。实验结果表明,在大角度低分辨率条件下,本文网络性能优于目前领先的人脸姿态矫正算法。

In non-cooperative scenes,to solve the problems of the poor reconstruction of faces and the low accuracy of face recognition due to the bad condition of posture deviations and the low image resolution,a triplet loss constrained generative adversarial network for reconstruction and recognition was proposed.The input faces were reconstructed firstly by encoder-decoder network,and then the reconstructed faces were recognized.In terms of training,a shortest distance triplet loss function was designed jointly with the adversarial mechanism,which increases the similarity intra-identification while enlarge the discrepancy inter-identification on the extracted feature representations.It is shown by the experiments that even under the condition of large angle deviations and low image resolution,the proposed algorithm performs better than currently leading face pose correction algorithms.