<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study explores fine-grained feature-based face attribute recognition techniques to enhance the accuracy of face recognition in low-resolution and complex environments. The article proposes a global feature extraction method and a local texture feature extraction method to extract global and regional features by enhancing feature reuse and information flow through dense connectivity and ShuffleNet V2 framework. Then, a multiscale feature exchange method is used to fuse different scale features to enhance the capture of detail information. Finally, efficient feature integration is achieved by the multiscale feature fusion method. Experimental results on the CK+ and FER2013 datasets show that the accuracy of this method on face expression recognition reaches 97.24% and 95.93%, respectively, and the average recognition accuracy in the face attribute recognition experiments on the CelebA dataset is 97.11%, which is significantly better than the comparison algorithm. In addition, the analysis of the recognition effect on low-resolution faces shows that this paper’s method achieves a recognition accuracy of 54.03% at a resolution of 15 × 15 and a high accuracy of over 99% at resolutions of 70 × 70 and above. These results show that the face attribute recognition technique based on fine-grained features proposed in this paper significantly improves recognition accuracy.</p>
</abstract>

This study explores fine-grained feature-based face attribute recognition techniques to enhance the accuracy of face recognition in low-resolution and complex environments. The article proposes a global feature extraction method and a local texture feature extraction method to extract global and regional features by enhancing feature reuse and information flow through dense connectivity and ShuffleNet V2 framework. Then, a multiscale feature exchange method is used to fuse different scale features to enhance the capture of detail information. Finally, efficient feature integration is achieved by the multiscale feature fusion method. Experimental results on the CK+ and FER2013 datasets show that the accuracy of this method on face expression recognition reaches 97.24% and 95.93%, respectively, and the average recognition accuracy in the face attribute recognition experiments on the CelebA dataset is 97.11%, which is significantly better than the comparison algorithm. In addition, the analysis of the recognition effect on low-resolution faces shows that this paper’s method achieves a recognition accuracy of 54.03% at a resolution of 15 × 15 and a high accuracy of over 99% at resolutions of 70 × 70 and above. These results show that the face attribute recognition technique based on fine-grained features proposed in this paper significantly improves recognition accuracy.

Research on Face Attribute Recognition Technology Based on Fine-Grained Features

Department of Criminal Science and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Research on Face Attribute Recognition Technology Based on Fine-Grained Features"}

This study explores fine-grained feature-based face attribute recognition techniques to enhance the accuracy of face recognition in low-resolution and complex...

Research on Face Attribute Recognition Technology Based on Fine-Grained Features

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 15 sty 2024

Przyjęty: 24 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0651

Słowa kluczowe
Information flow, Enhanced feature reuse, Multiscale feature exchange, Feature integration

© 2024 Yizhuo Gao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on Face Attribute Recognition Technology Based on Fine-Grained Features

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 15 sty 2024

Przyjęty: 24 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0651

Słowa kluczoweInformation flow, Enhanced feature reuse, Multiscale feature exchange, Feature integration

© 2024 Yizhuo Gao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Information flow, Enhanced feature reuse, Multiscale feature exchange, Feature integration