Department of Computer Science and Engineering, University at Buffalo, SUNY, NY 14260-2000, USA.
IEEE Trans Pattern Anal Mach Intell. 2010 Nov;32(11):1955-76. doi: 10.1109/TPAMI.2010.36.
Human age, as an important personal trait, can be directly inferred by distinct patterns emerging from the facial appearance. Derived from rapid advances in computer graphics and machine vision, computer-based age synthesis and estimation via faces have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as forensic art, electronic customer relationship management, security control and surveillance monitoring, biometrics, entertainment, and cosmetology. Age synthesis is defined to rerender a face image aesthetically with natural aging and rejuvenating effects on the individual face. Age estimation is defined to label a face image automatically with the exact age (year) or the age group (year range) of the individual face. Because of their particularity and complexity, both problems are attractive yet challenging to computer-based application system designers. Large efforts from both academia and industry have been devoted in the last a few decades. In this paper, we survey the complete state-of-the-art techniques in the face image-based age synthesis and estimation topics. Existing models, popular algorithms, system performances, technical difficulties, popular face aging databases, evaluation protocols, and promising future directions are also provided with systematic discussions.
人类年龄作为一个重要的个人特征，可以通过面部外观上出现的明显模式直接推断出来。由于其在现实世界中的应用（如法医艺术、电子客户关系管理、安全控制和监控、生物识别、娱乐和美容）的迅速发展，基于计算机的年龄合成和估计已成为当前特别流行的话题。年龄合成是指对个体面部进行美学渲染，实现自然老化和年轻化效果。年龄估计是指自动为面部图像贴上个体面部的准确年龄（年）或年龄组（年范围）标签。由于其特殊性和复杂性，这两个问题对于基于计算机的应用系统设计人员来说既具有吸引力又具有挑战性。在过去的几十年里，学术界和工业界都投入了大量的精力。本文综述了基于人脸图像的年龄合成和估计主题的最新技术。同时还提供了现有的模型、流行的算法、系统性能、技术难点、流行的人脸老化数据库、评估协议以及有前途的未来方向，并进行了系统的讨论。
