文献类型：期刊文献

英文题名：Multi-Task Deep Metric Learning with Boundary Discriminative Information for Cross-Age Face Verification

作者：Ni, Tongguang[1];Gu, Xiaoqing[1];Zhang, Cong[1];Wang, Weibo[2];Fan, Yiqing[3,4]

机构：[1]Changzhou Univ, Sch Informat Sci & Engn, Changzhou 213164, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[3]Univ Southern Calif, Viterbi Sch Engn, Los Angeles, CA 90089 USA;[4]Sichuan Int Studies Univ, Coll Int Educ, Chongqin 400031, Peoples R China

年份：2020

卷号：18

期号：2

起止页码：197

外文期刊名：JOURNAL OF GRID COMPUTING

收录：;EI(收录号：20195107875741);WOS:【SCI-EXPANDED(收录号:WOS:000540138000003)】；

语种：英文

外文关键词：Multi-task; Deep metric learning; Cross-age face verification; Discriminative information

摘要：Image based face verification has attracted extension attention in the fields of pattern recognition and intelligent vision. With difference in age, cross-age face verification from facial images remains a challenging work because of a large number of facial variations caused by shape, skin color and wrinkles and so on. This study proposes a multi-task deep metric learning with boundary discriminative information method called MDML-BDI. It learns a distance metric by exploring discriminative information among the interclass neighborhood samples, such that the distances between intraclass samples are as small as possible and that between interclass neighborhood samples are as far as possible. MDML-BDI learns hierarchical nonlinear transformations by integrating metric learning into the framework of multi-task deep neural network, such that a common shared layer shares the common transformation by multiple tasks, and the other independent layers learn individual task-special transformation for each task. Experimental results on FG-NET, CACD-VS and CALFW datasets show that MDML-BDI achieves satisfactory performance in terms of accuracy and receiver operating characteristic (ROC) curve.