Ordinal distribution regression for gait-based age estimation

作     者：Haiping ZHU Yuheng ZHANG Guohao LI Junping ZHANG Hongming SHAN Haiping ZHU;Yuheng ZHANG;Guohao LI;Junping ZHANG;Hongming SHAN

作者机构：Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Department of Biomedical Engineering Rensselaer Polytechnic Institute 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2020年第63卷第2期

页      面：21-34页

核心收录：

学科分类：12[管理学] 02[经济学] 07[理学] 08[工学] 070103[理学-概率论与数理统计] 0202[经济学-应用经济学] 020208[经济学-统计学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0714[理学-统计学（可授理学、经济学学位）] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported in part by National Natural Science Foundation of China (Grant No. 61673118) Shanghai Municipal Science and Technology Major Project (Grant No. 2018SHZDZX01) ZJLab Shanghai Pujiang Program (Grant No. 16PJD009) 

主　　题：computer vision deep learning ordinal distribution regression global and local features gaitbased age estimation 

摘      要：Computer vision researchers prefer to estimate age from face images because facial features provide useful information. However, estimating age from face images becomes challenging when people are distant from the camera or occluded. A person’s gait is a unique biometric feature that can be perceived efficiently even at a distance. Thus, gait can be used to predict age when face images are not ***, existing gait-based classification or regression methods ignore the ordinal relationship of different ages, which is an important clue for age estimation. This paper proposes an ordinal distribution regression with a global and local convolutional neural network for gait-based age estimation. Specifically, we decompose gait-based age regression into a series of binary classifications to incorporate the ordinal age ***, an ordinal distribution loss is proposed to consider the inner relationships among these classifications by penalizing the distribution discrepancy between the estimated value and the ground truth. In addition,our neural network comprises a global and three local sub-networks, and thus, is capable of learning the global structure and local details from the head, body, and feet. Experimental results indicate that the proposed approach outperforms state-of-the-art gait-based age estimation methods on the OULP-Age dataset.