AI-ED Department, Yncrea Ouest, 20 rue du Cuirassé de Bretagne, 29200 Brest, France.
Electronic and Micro-electronic Laboratory, Faculty of Sciences of Monastir, University of Monastir, Monastir 5000, Tunisia.
Sensors (Basel). 2020 Jan 7;20(2):342. doi: 10.3390/s20020342.
Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.
在过去的几十年中，人们对口译和同声传译的兴趣越来越浓厚。视频监控、犯罪识别、门禁系统以及无人驾驶和自动驾驶车辆等只是吸引各行业关注的几个具体应用案例。各种技术正在被开发出来，包括局部的、整体的和混合的方法，这些方法只使用少数面部图像特征或整个面部特征来提供面部图像描述。本调查的主要贡献在于回顾每种方法的一些知名技术，并对其类别进行分类。在本文中，通过列出这些方案在鲁棒性、准确性、复杂性和辨别力方面的优缺点，详细比较了这些技术。文中提到的一个有趣特征是关于用于人脸识别的数据库。给出了最常用的数据库概述，包括监督学习和无监督学习的数据库。本文还给出了最有趣的技术的数值结果，并结合这些技术处理的实验和挑战进行了说明。最后，本文就人脸识别技术的未来发展方向进行了深入的讨论。
