A review of deep convolutional neural networks in mobile face recognition
With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
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Kassel University Press
2023
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| Online Access: | https://eprints.ums.edu.my/id/eprint/38783/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/38783/2/FULL%20TEXT.pdf |
| _version_ | 1811136358851280896 |
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| author | Jing Chi Chin Kim On Haopeng Zhang Soo See Chai |
| author_facet | Jing Chi Chin Kim On Haopeng Zhang Soo See Chai |
| author_sort | Jing Chi |
| collection | UMS |
| description | With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably improved security and surveillance capabilities by providing automated recognition solutions, such as traffic surveillance, access control devices, biometric security systems, and attendance systems. However, there is still room for improvement in this field. This paper discusses several classic CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet, as well as lightweight models for mobile-based applications, such as MobileNet, ShuffleNet, and EfficientNet. Additionally, deep CNN-based face recognition models, such as DeepFace, DeepID, FaceNet, and SphereFace, are explored, along with their architectural characteristics, advantages, disadvantages, and recognition accuracy. The results indicate that many scholars are researching lightweight face recognition, but applying it to mobile devices is impractical due to high computational costs. Furthermore, noise label learning is not robust in actual scenarios, and unlabeled face learning is expensive in manual labeling. Finally, this paper concludes with a discussion of the current problems faced by face recognition technology and its potential future directions for development. |
| first_indexed | 2024-09-24T00:49:19Z |
| format | Article |
| id | ums.eprints-38783 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-09-24T00:49:19Z |
| publishDate | 2023 |
| publisher | Kassel University Press |
| record_format | dspace |
| spelling | ums.eprints-387832024-06-07T08:22:27Z https://eprints.ums.edu.my/id/eprint/38783/ A review of deep convolutional neural networks in mobile face recognition Jing Chi Chin Kim On Haopeng Zhang Soo See Chai Q300-390 Cybernetics QA75.5-76.95 Electronic computers. Computer science With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably improved security and surveillance capabilities by providing automated recognition solutions, such as traffic surveillance, access control devices, biometric security systems, and attendance systems. However, there is still room for improvement in this field. This paper discusses several classic CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet, as well as lightweight models for mobile-based applications, such as MobileNet, ShuffleNet, and EfficientNet. Additionally, deep CNN-based face recognition models, such as DeepFace, DeepID, FaceNet, and SphereFace, are explored, along with their architectural characteristics, advantages, disadvantages, and recognition accuracy. The results indicate that many scholars are researching lightweight face recognition, but applying it to mobile devices is impractical due to high computational costs. Furthermore, noise label learning is not robust in actual scenarios, and unlabeled face learning is expensive in manual labeling. Finally, this paper concludes with a discussion of the current problems faced by face recognition technology and its potential future directions for development. Kassel University Press 2023 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/38783/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/38783/2/FULL%20TEXT.pdf Jing Chi and Chin Kim On and Haopeng Zhang and Soo See Chai (2023) A review of deep convolutional neural networks in mobile face recognition. International Journal of Interactive Mobile Technologies, 17 (23). pp. 1-16. ISSN 1865-7923 https://doi.org/10.3991/ijim.v17i23.40867 |
| spellingShingle | Q300-390 Cybernetics QA75.5-76.95 Electronic computers. Computer science Jing Chi Chin Kim On Haopeng Zhang Soo See Chai A review of deep convolutional neural networks in mobile face recognition |
| title | A review of deep convolutional neural networks in mobile face recognition |
| title_full | A review of deep convolutional neural networks in mobile face recognition |
| title_fullStr | A review of deep convolutional neural networks in mobile face recognition |
| title_full_unstemmed | A review of deep convolutional neural networks in mobile face recognition |
| title_short | A review of deep convolutional neural networks in mobile face recognition |
| title_sort | review of deep convolutional neural networks in mobile face recognition |
| topic | Q300-390 Cybernetics QA75.5-76.95 Electronic computers. Computer science |
| url | https://eprints.ums.edu.my/id/eprint/38783/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/38783/2/FULL%20TEXT.pdf |
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