Deep learning technology has enabled successful modeling of complex facial features when high-quality images are available. Nonetheless, accurate modeling and recognition of human faces in real-world scenarios “on the wild” or under adverse conditions remains an open problem. Consequently, a plethora of novel deep network architectures addressing issues related to low-quality images, varying pose, illumination changes, emotional expressions, etc., have been proposed and studied over the last few years.This survey presents a comprehensive analysis of the latest developments in the field. A conventional deep face recognition system entails several main components: deep network, optimization loss function, classification algorithm, and train data collection. Aiming at providing a complete and comprehensive study of such complex frameworks, this paper first discusses the evolution of related network architectures. Next, a comparative analysis of loss functions, classification algorithms, and face datasets is given. Then, a comparative study of state-of-the-art face recognition systems is presented. Here, the performance of the systems is discussed using three benchmarking datasets with increasing degrees of complexity. Furthermore, an experimental study was conducted to compare several openly accessible face recognition frameworks in terms of recognition accuracy and speed.

中文翻译：

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博学的面孔：一项调查

当可获得高质量图像时，深度学习技术已使成功的复杂面部特征建模成为可能。尽管如此，在“野外”或不利条件下的真实场景中准确建模和识别人脸仍然是一个未解决的问题。因此，最近几年提出并研究了许多新颖的深度网络架构，以解决与低质量图像，姿势变化，照度变化，情感表达等相关的问题。本次调查对最新技术进行了全面分析该领域的发展。传统的深脸识别系统包含几个主要组件：深层网络，优化损失函数，分类算法和训练数据收集。旨在对此类复杂框架进行完整而全面的研究，本文首先讨论相关网络体系结构的发展。接下来，给出了损失函数，分类算法和面部数据集的比较分析。然后，对最先进的人脸识别系统进行了比较研究。在这里，使用三个基准数据集来讨论系统的性能，这些基准数据集的复杂度在不断提高。此外，进行了一项实验研究，以比较识别准确性和速度方面的几个可公开访问的面部识别框架。系统的性能将使用三个基准测试数据集进行讨论，而这些数据集的复杂程度将不断提高。此外，进行了一项实验研究，以比较识别准确性和速度方面的几个可公开访问的面部识别框架。系统的性能将使用三个基准测试数据集进行讨论，而这些数据集的复杂程度将不断提高。此外，进行了一项实验研究，以比较识别准确性和速度方面的几个可公开访问的面部识别框架。