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Mobi-FaceNeXt RESEARCH ON AN EFFICIENT FACE RECOGNITION ALGORITHM BASED ON LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORKS.

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Title: Mobi-FaceNeXt RESEARCH ON AN EFFICIENT FACE RECOGNITION ALGORITHM BASED ON LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORKS.
Authors: YUAN, Bin1, DU, Chang-Qing1 ducq@mail.qjnu.edu.cn, LI, Zi-Tian1
Source: Thermal Science. 2026, Vol. 30 Issue 2A, p1191-1201. 11p.
Subjects: Convolutional neural networks, Embedded computer systems, Loss functions (Statistics), Human facial recognition software, Deep learning
Abstract: The advent of deep learning and convolutional neural networks, in conjunction with the unremitting expansion and refinement of face recognition datasets, has precipitated a substantial advancement in face recognition technology based on convolutional neural networks. Nevertheless, in real-world implementation scenarios, numerous disadvantages persist in the deployment of facial recognition technology. The present study focuses on the research of face recognition algorithms based on lightweight convolutional neural networks. A thorough analysis of prevalent facial recognition architectures is conducted, encompassing an examination of numerous network models. The integration of diverse network models strengths is achieved to engineer a lightweight network, designated as Mobi-FaceNeXt, for the purpose of facial feature extraction. While ensuring the accuracy of face recognition, efforts are made to minimize network parameters and computational load. This makes the algorithm deployable on general embedded platforms and devices with limited computing and storage resources. This has significant practical engineering implications. In the research, a joint loss function of MagFace Loss and Center Loss is used for training, and the processed MS-Celeb-1M dataset is utilized to enhance the learning ability of the deep face recognition model for facial features. Depth-wise separable convolution is employed to reduce parameters and computations, and the algorithm is optimized to enhance the network processing speed, thereby facilitating the extraction of facial feature information. The experimental results demonstrate that the Mobi-FaceNeXt model can achieve a superior level of face recognition accuracy while maintaining a low level of network computations and parameters. The technology in question has the capacity to satisfy the requirements of embedded devices and to extract facial feature information with greater efficiency. This suggests a broad range of potential applications. [ABSTRACT FROM AUTHOR]
Copyright of Thermal Science is the property of Society of Thermal Engineers of Serbia and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: The advent of deep learning and convolutional neural networks, in conjunction with the unremitting expansion and refinement of face recognition datasets, has precipitated a substantial advancement in face recognition technology based on convolutional neural networks. Nevertheless, in real-world implementation scenarios, numerous disadvantages persist in the deployment of facial recognition technology. The present study focuses on the research of face recognition algorithms based on lightweight convolutional neural networks. A thorough analysis of prevalent facial recognition architectures is conducted, encompassing an examination of numerous network models. The integration of diverse network models strengths is achieved to engineer a lightweight network, designated as Mobi-FaceNeXt, for the purpose of facial feature extraction. While ensuring the accuracy of face recognition, efforts are made to minimize network parameters and computational load. This makes the algorithm deployable on general embedded platforms and devices with limited computing and storage resources. This has significant practical engineering implications. In the research, a joint loss function of MagFace Loss and Center Loss is used for training, and the processed MS-Celeb-1M dataset is utilized to enhance the learning ability of the deep face recognition model for facial features. Depth-wise separable convolution is employed to reduce parameters and computations, and the algorithm is optimized to enhance the network processing speed, thereby facilitating the extraction of facial feature information. The experimental results demonstrate that the Mobi-FaceNeXt model can achieve a superior level of face recognition accuracy while maintaining a low level of network computations and parameters. The technology in question has the capacity to satisfy the requirements of embedded devices and to extract facial feature information with greater efficiency. This suggests a broad range of potential applications. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Thermal Science is the property of Society of Thermal Engineers of Serbia and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Loss functions (Statistics)
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      – SubjectFull: Human facial recognition software
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      – TitleFull: Mobi-FaceNeXt RESEARCH ON AN EFFICIENT FACE RECOGNITION ALGORITHM BASED ON LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORKS.
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              Text: 2026
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