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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: We present the development of face processing methods in chronological order in Fig. 12. As we can see from the figure, most papers attempted to perform face processing by autoencoder model in 2014 and 2015; while 3D model played an important role in 2016. GAN [40] has drawn substantial attention from the deep learning and computer vision community since it was first proposed by Goodfellow et al. It can be used in different fields and was also introduced into face processing in 2017. GAN can be used to perform “one-to-many augmentation” and “many-to-one normalization”, and it broke the limit that face synthesis should be done under supervised way. Although GAN has not been widely used in face processing for training and recognition, it has great latent capacity for preprocessing, for example, Dual-Agent GANs (DA-GAN) [56] won the 1st places on verification and identification tracks in the NIST IJB-A 2017 FR competitions.

Question: What are the different approaches that have been used in face recognition technology over the years?
Starting from the 1990s to the 2000s, holistic approaches were the most prominent direction in face recognition, Later on, local-feature-based face recognition was introduced. In the 2010s, shallow learning-based-local-descriptors were used. In 2014, the DeepFace, a deep learning-based model, was invented. And ever since, the state-of-the-art techniques were from deep learning-based approaches.