Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (24): 27-38.DOI: 10.3778/j.issn.1002-8331.2107-0043

• Research Hotspots and Reviews • Previous Articles     Next Articles

Advances of Age Progression Based Cross-Age Face Recognition

LI Junyao, LI Zhihui, XIE Lanchi, HOU Xinyu, YE Dong   

  1. 1.Department of Criminal Science and Technology, Jiangsu Police Institute, Nanjing 210000, China
    2.Institute of Forensic Science, the Ministry of Public Security, Beijing 100032, China
  • Online:2021-12-15 Published:2021-12-13



  1. 1.江苏警官学院 刑事科学技术系,南京 210000
    2.公安部物证鉴定中心,北京 100032


Cross-age face recognition is one of the most difficult problems in face recognition at present. Face features will change with age, resulting in a decrease in recognition accuracy. The face recognition based on the generated aging images obtained from aging models is a popular solution for this problem. With the widespread application of computer technology and deep learning, the authenticity, aging effect, and algorithm efficiency of face aging have been significantly improved. This paper systematically reviews the current research status of cross-age face recognition based on aging models. The aging methods are sorted out in detail, the method evolution of aging models and the advantages and disadvantages of various methods are systematically introduced, and the existing model evaluation methods are summarized. In addition, the existing datasets that can be used for cross-age face recognition are introduced in detail, and a comparative analysis is made in terms of data volume, age span, age accuracy, and use of the data set. For the purpose of practical applications, the problems to be solved in cross-age face recognition based on the aging model are analyzed and discussed. Moreover, the future research directions are predicted and prospected.

Key words: face aging, cross-age, deep learning, face recognition



关键词: 人脸老化, 跨年龄, 深度学习, 人脸识别