A survey on deep learning based face recognition

By Guo-qing Guo, Na Zhang. Computer Vision and Image Understanding, 2019

451 citations recorded by the source corpus.

Abstract Deep learning, in particular the deep convolutional neural networks, has received increasing interests in face recognition recently, and a number of deep learning methods have been proposed. This paper summarizes about 330 contributions in this area. It reviews major deep learning concepts pertinent to face image analysis and face recognition, and provides a concise overview of studies on specific face recognition problems, such as handling variations in pose, age, illumination, expression, and heterogeneous face matching. A summary of databases used for deep face recognition is given as well. Finally, some open challenges and directions are discussed for future research.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a97f957df37cd17dadc791ea7b85b694361e5d13

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Citations in the corpus

Past, Present, and Future of Face Recognition: A Review (2020, methodology): “Nonetheless, they need a significant amount of training data to perform well [9].”
Channel Augmented Joint Learning for Visible-Infrared Recognition (2021, background): “Identity recognition (person re-identification [22], face recognition [11, 19]) systems have achieved significant success recently.”
Generative Adversarial Networks for Face Generation: A Survey (2022, background): “They focus on network evolution from traditional to deep learning based CNNs [51, 99].”