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

Abstract:

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