ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (12): 2760-2782.doi: 10.7544/issn1000-1239.2021.20200870

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Survey on Deep Learning Based Facial Attribute Recognition Methods

Lai Xinyu1,2, Chen Si1,2, Yan Yan3, Wang Dahan1,2, Zhu Shunzhi1,2   

  1. 1(School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, Fujian 361024);2(Fujian Key Laboratory of Pattern Recognition and Image Understanding(Xiamen University of Technology), Xiamen, Fujian 361024);3(School of Informatics, Xiamen University, Xiamen, Fujian 361005)
  • Online:2021-12-01
  • Supported by: 
    This work was supported by the General Program of the National Natural Science Foundation of China (62071404, 61773325), the General Program of the Natural Science Foundation of Fujian Province (2021J011185, 2020J01001), the Science and Technology Planning Project of Fujian Province (2020H0023), and the Youth Innovation Foundation of Xiamen City (3502Z20206068).

Abstract: Facial attribute recognition is one of the most popular research topics in computer vision and pattern recognition, and has great research significance of analyzing and understanding facial images. At the same time, it has a wide range of practical application value in many fields such as image retrieval, face recognition, micro-expression recognition and recommendation system. With the rapid development of deep learning, a large number of deep learning based facial attribute recognition (termed DFAR) methods have been put forward by domestic and foreign scholars. First the overall process of the facial attribute recognition method is described. Then, according to the different mechanisms of model construction, the part-based and holistic DFAR methods are reviewed and discussed in detail, respectively. Specifically, the part-based DFAR methods are classified according to whether or not to adopt the regular area localization technique, while the holistic DFAR methods are distinguished from the perspectives of single-task learning and multi-task learning, where multi-task learning based DFAR methods are further subdivided according to whether the attribute grouping strategy is used. Next, several popular databases and evaluation metrics on facial attribute recognition are introduced, and the performance of the state-of-the-art DFAR methods is compared and analyzed. Finally, the future research directions of the DFAR methods are provided.

Key words: facial attribute recognition, deep learning, multi-label learning, single-task learning, multi-task learning

CLC Number: