Face recognition is one of the most fundamental and long-standing topics in computer vision community. With the recent developments of deep convolutional neural networks and large-scale datasets, deep face recognition has made remarkable progress and been widely used in the real-world applications. Given a natural image or video frame as input, an end-to-end deep face recognition system outputs the face feature for recognition. To achieve this, the whole system is generally built with three key elements: face detection, face alignment, and face representation. The face detection locates faces in the image or frame. Then, the face alignment is proceeded to calibrate the faces to a canonical view and crop them to a normalized pixel size. Finally, in the stage of face representation, the discriminative features are extracted from the preprocessed faces for recognition. All of the three elements are fulfilled by deep convolutional neural networks. In this paper, we present a comprehensive survey about the recent advances of every element of the end-to-end deep face recognition, since the thriving deep learning techniques have greatly improved the capability of them. To start with, we introduce an overview of the end-to-end deep face recognition, which, as mentioned above, includes face detection, face alignment, and face representation. Then, we review the deep learning based advances of each element, respectively, covering many aspects such as the up-to-date algorithm designs, evaluation metrics, datasets, performance comparison, existing challenges, and promising directions for future research. We hope this survey could bring helpful thoughts to one for better understanding of the big picture of end-to-end face recognition and deeper exploration in a systematic way.

翻译：面部识别是计算机视觉界最基本和最长期的话题之一。 随着深层神经神经网络和大型数据集的最新发展,深刻的面部识别取得了显著的进展,并被广泛用于真实世界的应用中。以自然的图像或视频框架作为投入,一个端到端的面部识别系统产出了需要认识的面部特征。为此,整个系统通常由三个关键要素组成:面部检测、面部对齐和面部代表。面部检测在图像或框架中发现面部识别面部。然后,面部对面部的调整将面部对准成一个有帮助的直观观点,并将脸部识别成一个正常的像体型。最后,在面部展示一个有系统化的面部的面部认知中,我们从一个更清晰的面部对面部的认知中,我们从一个更深刻的面部对面部和后方面面面部的认知中,我们从一个更深刻的面部对面部的认知,我们从一个更深刻的面面部对面部的图像的认知中,我们从一个部的认知到后头的图像的认知,我们从一个部的审视到后头的认知到后头的认知中,每个部的认知,我们对面部的认知,每个部的认知,我们对面部的认知,每个部的视觉的认知,从一个部的认知,从一个部的认知到对面部的认知到后部的深度的认知,我们的审视的认知,对面部的认知,对面部的认知,对面部的认知到后部的认知,对面部的认知,对面部的认知,对面部的认知到后部的一面部的深度的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,对面部的认知,我们的认知,对面部的认知,对面部