当前位置: X-MOL 学术IEEE Trans. Inform. Forensics Secur. › 论文详情
Adaptive Convolution Local and Global Learning for Class-Level Joint Representation of Facial Recognition With a Single Sample Per Data Subject
IEEE Transactions on Information Forensics and Security ( IF 6.211 ) Pub Date : 2020-01-13 , DOI: 10.1109/tifs.2020.2965301
Meng Yang; Wei Wen; Xing Wang; Linlin Shen; Guangwei Gao

Due to the absence of training samples and intraclass variation, the extraction of discriminative facial features and construction of powerful classifiers have bottlenecks in improving the performance of facial recognition (FR) with a single sample per data subject (SSPDS). In this paper, we propose to learn regional adaptive convolution features that are locally and globally discriminative to facial identity and robust to facial variation. Then, a novel class-level joint representation framework is presented to exploit the distinctiveness and class-level commonality of different facial features. In the proposed class-level joint representation with regional adaptive convolution features (CJR-RACF), both discriminative facial features that are robust to facial variations and powerful representations for classification with generic facial variations have been fully exploited. Furthermore, the gallery discrimination is extracted by our proposed weight-embedded supervision in the training phase (denoted by CJR-RACF w ), which is conducive to more specific features for FR with SSPDS. CJR-RACF and CJR-RACF w have been evaluated on several popular databases, including the large-scale CMU Multi-PIE, LFW, Megaface, and VGGFace datasets. Experimental results demonstrate the much higher robustness and effectiveness of the proposed methods compared to the state-of-the-art methods.
更新日期:2020-02-11

 

全部期刊列表>>
全球疫情及响应:BMC Medicine专题征稿
欢迎探索2019年最具下载量的化学论文
新版X-MOL期刊搜索和高级搜索功能介绍
化学材料学全球高引用
ACS材料视界
南方科技大学
x-mol收录
南方科技大学
自然科研论文编辑服务
上海交通大学彭文杰
中国科学院长春应化所于聪-4-8
武汉工程大学
课题组网站
X-MOL
深圳大学二维材料实验室张晗
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug