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Context: Face recognition (FR) has been the prominent biometric technique for identity authentication and has been widely used in many areas, such as military, finance, public security and daily life. FR has been a long-standing research topic in the CVPR community. In the early 1990s, the study of FR became popular following the introduction of the historical Eigenface approach [1]. The milestones of feature-based FR over the past years are presented in Fig. 1, in which the times of four major technical streams are highlighted. The holistic approaches derive the low-dimensional representation through certain distribution assumptions, such as linear subspace [2][3][4], manifold [5][6][7], and sparse representation [8][9][10][11]. This idea dominated the FR community in the 1990s and 2000s. However, a well-known problem is that these theoretically plausible holistic methods fail to address the uncontrolled facial changes that deviate from their prior assumptions. In the early 2000s, this problem gave rise to local-feature-based FR. Gabor [12] and LBP [13], as well as their multilevel and high-dimensional extensions [14][15][16], achieved robust performance through some invariant properties of local filtering. Unfortunately, handcrafted features suffered from a lack of distinctiveness and compactness. In the early 2010s, learning-based local descriptors were introduced to the FR community [17][18][19], in which local filters are learned for better distinctiveness and the encoding codebook is learned for better compactness. However, these shallow representations still have an inevitable limitation on robustness against the complex nonlinear facial appearance variations. In general, traditional methods attempted to recognize human face by one or two layer representations, such as filtering responses, histogram of the feature codes, or distribution of the dictionary atoms. The research community studied intensively to separately improve the preprocessing, local descriptors, and feature transformation, but these approaches improved FR accuracy slowly. What’s worse, most methods aimed to address one aspect of unconstrained facial changes only, such as lighting, pose, expression, or disguise. There was no any integrated technique to address these unconstrained challenges integrally. As a result, with continuous efforts of more than a decade, “shallow” methods only improved the accuracy of the LFW benchmark to about 95% [15], which indicates that “shallow” methods are insufficient to extract stable identity feature invariant to real-world changes. Due to the insufficiency of this technical, facial recognition systems were often reported with unstable performance or failures with countless false alarms in real-world applications. In 2014, DeepFace [20] achieved the SOTA accuracy on the famous LFW benchmark [23], approaching human performance on the unconstrained condition for the first time (DeepFace: 97.35% vs. Human: 97.53%), by training a 9-layer model on 4 million facial images. Inspired by this work, research focus has shifted to deep-learning-based approaches, and the accuracy was dramatically boosted to above 99.80% in just three years. Deep learning technique has reshaped the research landscape of FR in almost all aspects such as algorithm designs, training/test datasets, application scenarios and even the evaluation protocols. Therefore, it is of great significance to review the breakthrough and rapid development process in recent years. There have been several surveys on FR [24, 25, 26, 27, 28] and its subdomains, and they mostly summarized and compared a diverse set of techniques related to a specific FR scene, such as illumination-invariant FR [29], 3D FR [28], pose-invariant FR [30][31]. Unfortunately, due to their earlier publication dates, none of them covered the deep learning methodology that is most successful nowadays. This survey focuses only on recognition problem, and one can refer to Ranjan et al. [32] for a brief review of a full deep FR pipeline with detection and alignment, or refer to Jin et al. [33] for a survey of face alignment. Specifically, the major contributions of this survey are as follows: Despite the high accuracy in the LFW [23] and Megaface [44, 164] benchmarks, the performance of FR models still hardly meets the requirements in real-world application. A conjecture in industry is made that results of generic deep models can be improved simply by collecting big datasets of the target scene. However, this holds only to a certain degree. More and more concerns on privacy may make the collection and human-annotation of face data become illegal in the future. Therefore, significant efforts have been paid to design excellent algorithms to address the specific problems with limited data in these realistic scenes. In this section, we present several special algorithms of FR. •Privacy-preserving face recognition. With the leakage of biological data, privacy concerns are raising nowadays. Facial images can predict not only demographic information such as gender, age, or race, but even the genetic information [297]. Recently, the pioneer works such as Semi-Adversarial Networks [298, 299, 285] have explored to generate a recognizable biometric templates that can hidden some of the private information presented in the facial images. Further research on the principles of visual cryptography, signal mixing and image perturbation to protect users’ privacy on stored face templates are essential for addressing public concern on privacy. •Understanding deep face recognition. Deep face recognition systems are now believed to surpass human performance in most scenarios [300]. There are also some interesting attempts to apply deep models to assist human operators for face verification [183][300]. Despite this progress, many fundamental questions are still open, such as what is the “identity capacity” of a deep representation [301]? Why deep neural networks, rather than humans, are easily fooled by adversarial samples? While bigger and bigger training dataset by itself cannot solve this problem, deeper understanding on these questions may help us to build robust applications in real world. Recently, a new benchmark called TALFW has been proposed to explore this issue [93].

Question: What are some common methods used in facial recognition and how do they compare in terms of effectiveness and challenges?
There are broadly 4 methods that are used in FR. Holistic methods were the first-ever attempt to solve the FR problem. But they were too primitive and could not account for uncontrolled facial changes that did not fit its assumptions. Then, there are local feature-based methods that try to extract invariant properties with local filtering. However, although better than holistic methods, these are also short of complexity and capacity to address the vastness of facial appearances. The first learning-based methods also lacked the robustness to address the non-linearity and complexity of FR. Also, the efforts that were made in this direction were too scattered and there were no traditional methods that could address the FR problem entirely. Afterward, deep learning based methods were introduced which surpassed the human ability in FR. Unfortunately, these methods are prone to adversarial noises and need large datasets. In particular, designing bigger and bigger datasets is becoming a privacy issue, that is yet to be handled by deep FR models.