Biometric systems have the goal of measuring and analyzing the unique physical or behavioral characteristics of an individual. The main feature of biometric systems is the use of bodily structures with distinctive characteristics. In the literature, there are biometric systems that use physiological features (fingerprint, iris, palm print, face, etc.) as well as systems that use behavioral characteristics (signature, walking, speech patterns, facial dynamics, etc.) Recently, facial biometrics has been one of the most preferred biometric data since it generally does not require the cooperation of the user and can be obtained without violating the personal private space. In this paper, the methods used to obtain and classify facial biometric data in the literature have been summarized. We give a taxonomy of image-based and video-based face recognition methods, outline the major historical developments, and the main processing steps. Popular data sets that have been used for face recognition by researchers are also reviewed. We also cover the recent deep-learning based methods for face recognition and point out possible directions for future research.

生物识别系统的目标是测量和分析个人独特的身体或行为特征。生物识别系统的主要特征是使用具有独特特征的身体结构。在文献中，存在使用生理特征（指纹，虹膜，掌纹，面部等）的生物识别系统，以及使用行为特征（签名，行走，语音模式，面部动态等）的系统。生物统计数据一直是最优选的生物统计数据之一，因为它通常不需要用户的配合，并且可以在不违反个人私人空间的情况下获得。在本文中，总结了文献中用于获取和分类面部生物特征数据的方法。我们给出了基于图像和基于视频的面部识别方法的分类法，概述了主要的历史发展以及主要的处理步骤。还审查了研究人员用于面部识别的流行数据集。我们还将介绍最近基于深度学习的人脸识别方法，并指出未来研究的可能方向。