计算机科学与探索

• 学术研究 •    

融合注意力机制和课程式学习的人脸识别方法

王海勇, 潘海涛, 刘贵楠   

  1. 南京邮电大学计算机学院 南京邮电大学智慧校园研究中心, 南京 210003

Face recognition method based on attention mechanism and curriculum learning

WANG Haiyong, PAN Haitao, LIU Guinan   

  1. College of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

摘要: 针对当前人脸识别算法中提取的人脸特征可区分性不强、难易样本区分度不够的问题,提出一种融合注意力机制和课程式学习的人脸识别算法(Efficient Cooperative Attention and Curriculum Face,ECACFace)。该算法提出一种高效的空间通道注意力模块(Efficient Spatial Channel Attention,ESCA)并将其融入特征提取网络的基本模块中,使用高效的通道注意力模块(Efficient Channel Attention,ECA)获取通道关注度并在ECA之后加入空间注意力模块,在关注图像通道信息的基础之上进一步获取空间关注度,从而得到信息更加丰富的人脸特征向量用于人脸分类。同时在训练时引入基于课程式学习的损失函数,做到在训练过程中区分难易样本,并在前期着重训练简单样本,后期着重训练困难样本,实现有区分度的样本学习。在CASIA-WebFace数据集上训练基于轻量级网络和浅层网络的ECACFace,与原始网络相比有超过1.5%的精度提升。在百万规模的MS1MV2上训练基于深层网络的ECACFace,在CPLFW上比ArcFace精度提升了1.14%,实验结果表明融合ESCA模块和基于课程式学习的损失函数能够进一步提升人脸识别性能。

关键词: 人脸识别, 特征提取, 课程式学习, 注意力机制

Abstract: Aiming at the problems that the facial features extracted from current face recognition algorithms are not distinguishable and the discrimination of difficult and easy samples is not enough, a face recognition algorithm combining attention mechanism and curriculum learning is proposed which called Efficient Cooperative Attention and Curriculum Face (ECACFace). The algorithm proposes an efficient spatial channel attention module (ESCA) and integrates it into the basic module of the feature extraction network, the efficient channel attention module (ECA) is used to obtain the channel attention and the spatial attention module is added after the ECA, on the basis of paying attention to the image channel information, the spatial attention is further obtained, and the face feature vector with richer information is obtained for face classification. At the same time, the loss function based on curriculum learning is introduced to distinguish the difficult and easy samples in the training process, the simple samples are trained in the early stage and the difficult samples are trained in the later stage to realize the discriminative sample learning. Training ECACFace based on lightweight network and shallow network on CASIA WebFace dataset and it has an accuracy improvement of more than 1.5% compared with the original network. ECACFace based on deep network is trained on MS1MV2 which has millions of data, and the accuracy tested on CPLFW was increased by 1.14% compared with ArcFace. The experimental results show that the cooperation of ESCA module and the loss function based on curriculum learning can further improve the performance of face recognition.

Key words: Face recognition, Feature extraction, Curriculum learning, Attention mechanism