目的 人脸年龄估计技术作为一种新兴的生物特征识别技术，已经成为计算机视觉领域的重要研究方向之一。随着深度学习的飞速发展，基于深度卷积神经网络的人脸年龄估计技术已成为研究热点。方法 本文以基于深度学习的真实年龄和表象年龄估计方法为研究对象，通过调研文献，分析了基于深度学习的人脸年龄估计方法的基本思想和特点，阐述其研究现状，总结关键技术及其局限性，对比了常见人脸年龄估计方法的性能，展望了未来的发展方向。结果 尽管基于深度学习的人脸年龄估计研究取得了巨大的进展，但非受限条件下年龄估计的效果仍不能满足实际需求，主要因为当前人脸年龄估计研究仍存在以下困难：1）引入人脸年龄估计的先验知识不足；2）缺少兼顾全局和局部细节的人脸年龄估计特征表达方法；3）现有人脸年龄估计数据集的限制；4）实际应用环境下的多尺度人脸年龄估计问题。结论 基于深度学习的人脸年龄估计技术已取得显著进展，但是由于实际应用场景复杂，容易导致人脸年龄估计效果不佳。对目前基于深度学习的人脸年龄估计技术进行全面综述，从而为研究者解决存在的问题提供便利。

Objective As an important part of human biometrics, age information has extensive application prospects in the fields of security monitoring, human-computer interaction, and video retrieval. As an emerging biometric recognition technology, age estimation technology based on face image is an important research subject in the fields of computer vision and face analysis. With the fast development of deep learning, the face age estimation method based on deep convolutional neural network has become a research hotspot in these fields. Method Real and apparent age estimation methods based on deep learning are reviewed based on extensive research and the latest achievements of relevant literature. The basic ideas and characteristics of various methods are analyzed. The research status, key technologies, and limitations based on various age estimation methods are summarized. The performance of various methods on common age estimation datasets is compared. Finally, existing major research problems are summarized and discussed, and potential future research directions are presented. Result Face age estimation can be divided into real and apparent age estimation according to the subjectivity and objectivity of age labeling, and it can be divided into age group estimation and age value estimation according to the accuracy of age labeling. With the deep convolutional neural network (DCNN) becoming a hotspot in the field of computer vision, from 5-conv 3-fc's AlexNet 33 to 16-conv 3-fc's VGG-19 network and from 21-conv 1-fc's GoogleNet to thousands of layers of ResNets, the learning ability and the depth of the network have improved considerably. An increasing number of face age estimation researchers are focusing on face age estimation based on DCNN with powerful feature extraction and learning capabilities. According to different views, face age estimation methods based on deep learning can be roughly divided into three categories:regression model, multi-class classification, and rank model. Regression model uses regression analysis to achieve age estimation by establishing a functional model that characterizes the age variation of faces. Regression-based age estimation methods may be affected by overfitting due to the randomness in the aging process and the fuzzy mapping between the appearance of the face and its actual age. The age of a person can be easily divided into several age groups. Age group estimation under unconstrained conditions has become a current research topic, and the multi-classification model is the main means of achieving age group estimation because the regression-based age estimation model has difficulty achieving convergence. Moreover, age group classification can meet the needs of most practical applications. The age estimation model based on the rank model regards the age label as a data sequence and converts the age estimation problem into a problem in which the age to be estimated is greater or less than a certain age, thereby transforming the age estimation problem into a series of binary classification problems. Other technologies in the field of computer vision are applied in face age estimation. Although various deep learning-based face age estimation methods have achieved considerable progress, the performance of age estimation fails to meet the practical needs of unconstrained age estimation because current face age estimation research continues to face the following difficulties and challenges:1) insufficient prior knowledge introduced to face age estimation methods; 2) lack of face age estimation feature representation that considers global and local details; 3) the limitations of existing face age estimation datasets; and 4) multi-scale face age estimation problems in practical application environments. Conclusion Deep learning-based face age estimation methods have achieved considerable progress, but they perform poorly due to the complexity of actual application scenarios. A comprehensive review of the current deep learning-based face age estimation techniques is needed to help researchers solve existing problems. Age estimation techniques based on face images are expected to play an important role in the future with the continued efforts of researchers and the in-depth development of related technologies.