Group-aware deep feature learning for facial age estimation

Highlights • We propose a group-aware deep feature learning (GA-DFL) method under the deep convolutional neural networks framework. With the learned nonlinear filters, the chronological age information can be well exploited. • We propose an overlapped coupled learning method to achieve the smoothness for the neighboring age groups. With this learning strategy, the age difference information on the age-group specific overlaps can be well measured. • We employ a multi-path deep CNN architecture to integrate multi-scale facial information into the learned face presentation to further improve the estimation performance. • Compared with most state-of-the-arts, experimental results show that our proposed methods have obtain significant performance on three released face aging datasets. Abstract In this paper, we propose a group-aware deep feature learning (GA-DFL) approach for facial age estimation. Unlike most existing methods which utilize hand-crafted descriptors for face representation, our GA-DFL method learns a discriminative feature descriptor per image directly from raw pixels for face representation under the deep convolutional neural networks framework. Motivated by the fact that age labels are chronologically correlated and the facial aging datasets are usually lack of labeled data for each person in a long range of ages, we split ordinal ages into a set of discrete groups and learn deep feature transformations across age groups to project each face pair into the new feature space, where the intra-group variances of positive face pairs from the training set are minimized and the inter-group variances of negative face pairs are maximized, simultaneously. Moreover, we employ an overlapped coupled learning method to exploit the smoothness for adjacent age groups. To further enhance the discriminative capacity of face representation, we design a multi-path CNN approach to integrate the complementary information from multi-scale perspectives. Experimental results show that our approach achieves very competitive performance compared with most state-of-the-arts on three public face aging datasets that were captured under both controlled and uncontrolled environments.

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Keywords Facial age estimation Deep learning Feature learning Biometrics

Hao Liu received the B.S. degree in software engineering from Sichuan University, China, in 2011 and the Engineering Master degree in computer technology from University of Chinese Academy of Sciences, China, in 2014. He is currently pursuing the Ph.D. degree at the department of automation, Tsinghua University. His research interests include face recognition, facial age estimation and deep learning.

Jiwen Lu received the B.Eng. degree in mechanical engineering and the M.Eng. degree in electrical engineering from the Xi'an University of Technology, Xi'an, China, and the Ph.D. degree in electrical engineering from the Nanyang Technological University, Singapore, in 2003, 2006, and 2012, respectively. He is currently an Associate Professor with the Department of Automation, Tsinghua University, Beijing, China. From March 2011 to November 2015, he was a Research Scientist with the Advanced Digital Sciences Center, Singapore. His current research interests include computer vision, pattern recognition, and machine learning. He has authored/co-authored over 130 scientific papers in these areas, where more than 50 papers are published in the IEEE Transactions journals and top-tier computer vision conferences. He serves/has served as an Associate Editor of Pattern Recognition Letters, Neurocomputing, and the IEEE Access, a Guest Editor of Pattern Recognition, Computer Vision and Image Understanding, Image and Vision Computing and Neurocomputing, and an elected member of the Information Forensics and Security Technical Committee of the IEEE Signal Processing Society. He is/was a Workshop Chair/Special Session Chair/Area Chair for more than 10 international conferences. He has given tutorials at several international conferences including ACCV’16, CVPR’15, FG’15, ACCV’14, ICME’14, and IJCB’14. He was a recipient of the First-Prize National Scholarship and the National Outstanding Student Award from the Ministry of Education of China in 2002 and 2003, the Best Student Paper Award from Pattern Recognition and Machine Intelligence Association of Singapore in 2012, the Top 10% Best Paper Award from IEEE International Workshop on Multimedia Signal Processing in 2014, and the National 1000 Young Talents Plan Program in 2015, respectively. He is a senior member of the IEEE.

Jianjiang Feng is an associate professor in the Department of Automation at Tsinghua University, Beijing. He received the B.S. and Ph.D. degrees from the School of Telecommunication Engineering, Beijing University of Posts and Telecommunications, China, in 2000 and 2007. From 2008 to 2009, he was a Post Doctoral researcher in the PRIP lab at Michigan State University. He is an Associate Editor of Image and Vision Computing. His research interests include fingerprint recognition and computer vision.

Jie Zhou received the BS and MS degrees both from the Department of Mathematics, Nankai University, Tianjin, China, in 1990 and 1992, respectively, and the Ph.D. degree from the Institute of Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology (HUST), Wuhan, China, in 1995. From then to 1997, he served as a postdoctoral fellow in the Department of Automation, Tsinghua University, Beijing, China. Since 2003, he has been a full professor in the Department of Automation, Tsinghua University. His research interests include computer vision, pattern recognition, and image processing. In recent years, he has authored more than 100 papers in peer-reviewed journals and conferences. Among them, more than 30 papers have been published in top journals and conferences such as the IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, and CVPR. He is an associate editor for the International Journal of Robotics and Automation and two other journals. He received the National Outstanding Youth Foundation of China Award. He is a senior member of the IEEE.

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