Face Recognition: From Traditional to Deep Learning Methods
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Abstract (arXiv)
Starting in the seventies, face recognition has become one of the most researched topics in computer vision and biometrics. Traditional methods based on hand-crafted features and traditional machine learning techniques have recently been superseded by deep neural networks trained with very large datasets. In this paper we provide a comprehensive and up-to-date literature review of popular face recognition methods including both traditional (geometry-based, holistic, feature-based and hybrid methods) and deep learning methods.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 31 Oct 2018.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 2.
4 papers.
- 2021Classic versus deep learning approaches to address computer vision challengescited by 1
- 2024Local and Global Feature Attention Fusion Network for Face Recognitioncited by 1
- 2021Deep learning for identification and face, gender, expression recognition under constraintscited by 0
- 2024Robotic Eye-in-hand Visual Servo Axially Aligning Nasopharyngeal Swabs with the Nasal Cavitycited by 0
- 2024Local and Global Feature Attention Fusion Network for Face Recognition25 Nov 2024
- 2024Robotic Eye-in-hand Visual Servo Axially Aligning Nasopharyngeal Swabs with the Nasal Cavity22 Aug 2024
- 2021Deep learning for identification and face, gender, expression recognition under constraints2 Nov 2021
- 2021Classic versus deep learning approaches to address computer vision challenges24 Jan 2021