期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 32, 期 4, 页码 1560-1574出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2020.2985099
关键词
Prototypes; Databases; Dictionaries; Face; Learning systems; Face recognition; Feature extraction; Contaminated biometric enrolment database; face recognition (FR); low-rank representation (LRR); single-sample per person (SSPP)
类别
资金
- National Natural Science Foundation of China [61672444]
- Hong Kong Baptist University (HKBU), Research Committee, Initiation Grant, Faculty Niche Research Areas (IG-FNRA) [RC-FNRA-IG/18-19/SCI/03]
- Innovation and Technology Fund of Innovation and Technology Commission of the Government of the Hong Kong [ITS/339/18]
- Shenzhen Science and Technology Innovation Commission (SZSTI) [JCYJ20160531194006833]
This article focuses on the problem of single-sample per person face recognition with a contaminated biometric enrolment database. The proposed IDGL method uses a semisupervised low-rank representation framework to recover prototypes and learn a representative variation dictionary, improving label estimation accuracy iteratively through a dynamic learning network. Experimental results have shown the superiority of IDGL over state-of-the-art counterparts.
This article focuses on a new and practical problem in single-sample per person face recognition (SSPP FR), i.e., SSPP FR with a contaminated biometric enrolment database (SSPP-ce FR), where the SSPP-based enrolment database is contaminated by nuisance facial variations in the wild, such as poor lightings, expression change, and disguises (e.g., wearing sunglasses, hat, and scarf). In SSPP-ce FR, the most popular generic learning methods will suffer serious performance degradation because the prototype plus variation (P+V) model used in these methods is no longer suitable in such scenarios. The reasons are twofold. First, the contaminated enrolment samples could yield bad prototypes to represent the persons. Second, the generated variation dictionary is simply based on the subtraction of the average face from generic samples of the same person and cannot well depict the intrapersonal variations. To address the SSPP-ce FR problem, we propose a novel iterative dynamic generic learning (IDGL) method, where the labeled enrolment database and the unlabeled query set are fed into a dynamic label feedback network for learning. Specifically, IDGL first recovers the prototypes for the contaminated enrolment samples via a semisupervised low-rank representation (SSLRR) framework and learns a representative variation dictionary by extracting the sample-specific corruptions from an auxiliary generic set. Then, it puts them into the P+V model to estimate labels for query samples. Subsequently, the estimated labels will be used as feedback to modify the SSLRR, thus updating new prototypes for the next round of P+V-based label estimation. With the dynamic learning network, the accuracy of the estimated labels is improved iteratively by virtue of the steadily enhanced prototypes. Experiments on various benchmark face data sets have demonstrated the superiority of IDGL over state-of-the-art counterparts.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据