A survey on deep learning based face recognition

• Published in: Computer vision and image understanding Vol. 189; p. 102805
• Main Authors: Guo, Guodong, Zhang, Na
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.12.2019
• Subjects: Artificial Neural Network; Autoencoder; Convolutional Neural Networks; Deep learning; Face recognition; Generative Adversarial Networks; Deep learning; Face recognition; Artificial Neural Network; Autoencoder; 41A10; 65D05; 65D17; Generative Adversarial Networks; Convolutional Neural Networks; 41A05
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1016/j.cviu.2019.102805

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. [Display omitted] •Presents a comprehensive survey of deep learning based face recognition methods.•Categorizes various approaches systematically.•Focuses on architectures, loss functions and other related things.•Includes specific issues on pose, expression, 3D, heterogeneous matching, etc.•Contains a broad review of related face databases.