Age Estimation Algorithm Based on Deep Cost Sensitive CNN
LI Daxiang1,2, MA Xuan2, REN Yaqiong2, LIU Ying1,2
1. School of Communications and Information Engineering, Xi′an University of Posts and Telecommunications, Xi′an 710121; 2. Key Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi′an University of Posts and Telecommunications, Xi′an 710121
Abstract Aiming at the problems of the imbalance of sample size in age estimation and the cost of misclassification between different classes, the cost sensitivity is embedded into the deep learning framework, and an age estimation algorithm based on deep cost sensitive convolutional neural networks (CNN) is proposed. Firstly, a loss function is established for each age category to solve the imbalance problem of the training samples. Then, a cost vector is defined to reflect the cost difference caused by misclassification between different classes, and an inverse cross entropy error function is constructed. Finally, the above methods are merged to derive a loss function for CNN to learn the robust face representation for age estimation during the training process. Experiments on different age estimation standard image sets verify the effectiveness of the proposed algorithm.
Fund:Supported by National Natural Science Foundation of China(No.61571361,61102095),General Program of International Cooperation and Exchange Foundation of Shaanxi Province(No.2017KW-013,2019JM-604)
Corresponding Authors:
LI Daxiang, Ph.D., associate professor. His research interests include image retrieval and image classification.
About author:: MA Xuan, master student. His research interests include facial age estimation; REN Yaqiong, master student. Her research interests include machine learning; LIU Ying, Ph.D., professor. Her research interests include image retrieval and image classification.
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2020, Vol. 33 
