UM
Single Sample Face Recognition via Learning Deep Supervised Autoencoders
Gao S.; Zhang Y.; Jia K.; Lu J.; Zhang Y.
2015
Source PublicationIEEE Transactions on Information Forensics and Security
ISSN15566013
Volume10Issue:10Pages:2108
AbstractThis paper targets learning robust image representation for single training sample per person face recognition. Motivated by the success of deep learning in image representation, we propose a supervised autoencoder, which is a new type of building block for deep architectures. There are two features distinct our supervised autoencoder from standard autoencoder. First, we enforce the faces with variants to be mapped with the canonical face of the person, for example, frontal face with neutral expression and normal illumination; Second, we enforce features corresponding to the same person to be similar. As a result, our supervised autoencoder extracts the features which are robust to variances in illumination, expression, occlusion, and pose, and facilitates the face recognition. We stack such supervised autoencoders to get the deep architecture and use it for extracting features in image representation. Experimental results on the AR, Extended Yale B, CMU-PIE, and Multi-PIE data sets demonstrate that by coupling with the commonly used sparse representation-based classification, our stacked supervised autoencoders-based face representation significantly outperforms the commonly used image representations in single sample per person face recognition, and it achieves higher recognition accuracy compared with other deep learning models, including the deep Lambertian network, in spite of much less training data and without any domain information. Moreover, supervised autoencoder can also be used for face verification, which further demonstrates its effectiveness for face representation. © 2005-2012 IEEE.
KeywordDeep architecture Face recognition Single training sample per person Supervised Auto-encoder
DOI10.1109/TIFS.2015.2446438
URLView the original
Language英语
The Source to ArticleScopus
全文获取链接
引用统计
被引频次[WOS]:74   [WOS记录]     [WOS相关记录]
Document TypeJournal article
专题University of Macau
推荐引用方式
GB/T 7714
Gao S.,Zhang Y.,Jia K.,et al. Single Sample Face Recognition via Learning Deep Supervised Autoencoders[J]. IEEE Transactions on Information Forensics and Security,2015,10(10):2108.
APA Gao S.,Zhang Y.,Jia K.,Lu J.,&Zhang Y..(2015).Single Sample Face Recognition via Learning Deep Supervised Autoencoders.IEEE Transactions on Information Forensics and Security,10(10),2108.
MLA Gao S.,et al."Single Sample Face Recognition via Learning Deep Supervised Autoencoders".IEEE Transactions on Information Forensics and Security 10.10(2015):2108.
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
Google Scholar
中相似的文章 Google Scholar
[Gao S.]的文章
[Zhang Y.]的文章
[Jia K.]的文章
Baidu academic
中相似的文章 Baidu academic
[Gao S.]的文章
[Zhang Y.]的文章
[Jia K.]的文章
Bing Scholar
中相似的文章 Bing Scholar
[Gao S.]的文章
[Zhang Y.]的文章
[Jia K.]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。