A Comparative Study Between Deep Learning and Traditional Machine Learning Techniques for Facial Biometric Recognition
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When the data size is large, deep learning algorithms outperform traditional machine learning algorithms.
“When the data size is large, several studies [11-14] have shown that DL algorithms outperform the traditional ML algorithms.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [13]
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On the Limitation of Pathological Iris Recognition: Neural Network Perspectives
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Self-citation
- Authors
- Finizola, J. S., Targino, J. M., Teodoro, F. G. S., & de Moraes Lima, C. A.
- Affiliations
- Universidade de São Paulo (BR)
- Corresponding author
- Finizola, Jonnathann Silva
- Targino, Jonas Mendonça
- Teodoro, Felipe Gustavo Silva
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 217-228
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2899781844
- Cited by
- 9
- References
- 20
- Citations by year
- 2024: 1 · 2023: 2 · 2021: 4 · 2020: 1 · 2019: 1
- FWCI
- 0.45
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- face recognition, face biometrics, convolutional neural networks, traditional machine learning, k-nearest neighbors, support vector machine, extreme learning machine, artificial neural networks, optimum-path forest, biometric identification, algorithm comparison
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