在许多现实的人脸识别场景，比如护照验证和登机口身份识别，通常很少甚至只有一个训练图像，而测试样本可能包含各种剧烈的脸部变化如光照、表情和遮挡，这就是所谓的单样本人脸识别问题。
如何在含有剧烈类内变化的人脸识别场景中取得较好的识别效果，成为当前许多人脸识别工作研究的重点问题。
针对这个问题，研究者们提出了许多单样本人脸识别的解决方案。
受到稀疏表示的启发，Wright等[1]首次将基于稀疏表示的分类方法（Sparse Representation Classification，SRC）用于人脸识别中并取得了很好的效果。
基于SRC的人脸识别方法背后的基本假设是待分类的人脸基于自编码器和稀疏表示的单样本人脸识别王钰1，2，刘凡1，2，王菲1，21.河海大学海岸灾害及防护教育部重点实验室，南京2100982.河海大学计算机与信息学院，南京210098摘要：单样本人脸识别因其在现实生活中的广泛应用而成为人脸识别领域的热门话题。
单张训练样本条件下训练样本的缺少和复杂的类内人脸表情、光照、遮挡变化给单样本人脸识别研究带来困难。
传统的基于稀疏表示的人脸识别方法需要大量的训练样本构成过完备的字典，因而在单样本条件下识别效果明显下滑。
针对这一问题，提出一种基于有监督自编码器的带变化人脸样本生成方法，在保留身份信息的同时自动生成带变化的人脸图像用于单样本条件下的字典扩充，一定程度上缓解了单样本条件下的欠采样问题，弥补了训练集和测试集间的人脸变化信息差异，使得传统的稀疏表示方法能够适用于单样本人脸识别问题。
在公共数据库上的实验结果不仅证明了该方法的有效性，而且对测试集中不同的人脸变化也展现出了较强的鲁棒性。
关键词：单样本人脸识别；有监督自编码器；稀疏表示；字典学习文献标志码：A中图分类号：TP399doi：10.3778/j.issn.1002-8331.2007-0312Autoencoder Based Sparse Representation for Single Sample Face RecognitionWANG Yu1，2,LIU Fan1，2,WANG Fei1，21.Key Laboratory of Ministry of Education for Coastal Disaster and Protection,College of Computer Information,HohaiUniversity,Nianjing210098,China2.College of Computer and Information,Hohai University,Nanjing210098,ChinaAbstract：Single sample face recognition has become a hot topic in the field of face recognition since its wide application in real life.The lack of training samples and the dramatic inter-class variations of facial expression,illumination,and occlusion make it difficult to study.The traditional face recognition method based on sparse representation needs a large number of training samples to construct an over-complete dictionary,so the recognition accuracy is significantly dropped under the single sample condition.To solve this problem,a supervised autoencoder based method is proposed to generate faces with variations,which can automatically generate face images with variations while preserving identity information for dictionary expansion under the condition of a single sample.To a certain extent,this method can alleviate the problem of under sampling under the condition of a single sample,and make up for the difference of face variance information between the training set and test set,making the traditional sparse representation method suitable for single sample face recognition.Experimental results on public databases not only prove the effectiveness of the method but also show strong robustness to different face variations in the test set.Key words：single sample face recognition;supervised auto-encoder;sparse representation;dictionary learning基金项目：江苏省自然科学基金（BK20191298）；河海大学海岸灾害及防护教育部重点实验室开放基金（201905）。
