Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Proceedings of the 4th International Conference on Signal Processing and Machine Learning
Series Vol. 45 , 15 March 2024
Open
Access | Article
Real-time face recognition method based on MTCNN-Inception-ResNet-v2-SVM model
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Abstract
With the widespread use of smart devices such as smartphones, facial recognition applications have experienced rapid growth. Additionally, breakthroughs in deep learning algorithms have led to the development of facial recognition technology based on deep convolutional networks, greatly improving recognition speed and accuracy. Therefore, this study proposes a real-time face recognition method based on the MTCNN-Inception-ResNet-v2-SVM model. In the facial detection phase, the MTCNN algorithm, which offers better overall performance compared to face detection algorithms such as OpenCV and Dlib, is utilized. Data augmentation and other methods are employed in the image preprocessing phase to enhance data diversity. The Inception-ResNet-v2 deep convolutional neural network is used as the backbone network for feature extraction in the facial recognition backbone network section. In the final classification stage, an SVM classifier is employed for the ultimate facial classification. Comparative analyses with models such as Inception-ResNet-v1, Inception-v3, and Inception-v4 are conducted in the backbone network section, determining that the Inception-ResNet-v2 model exhibits the best overall performance. The final result is a real-time face recognition model, MTCNN-Inception-ResNet-v2-SVM, with a high accuracy of 98.79% and fast processing speed.
Keywords
Deep Learning, Facial Recognition, Convolutional Neural Network, MTCNN, Inception-ResNet-v2
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 4th International Conference on Signal Processing and Machine Learning
- ISBN (Print)
- 978-1-83558-331-9
- ISBN (Online)
- 978-1-83558-332-6
- Published Date
- 15 March 2024
- Series
- Applied and Computational Engineering
- ISSN (Print)
- 2755-2721
- ISSN (Online)
- 2755-273X
- DOI
- 10.54254/2755-2721/45/20241677
- Copyright
- 15 March 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited