Paper
8 November 2023 Low resolution face image adaptive recognition method based on high order singular value decomposition and DNN model
Yingchun Du, Tingting Hou, Yongxin Zhang, Chao Wu, Xiao Wang
Author Affiliations +
Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129231H (2023) https://doi.org/10.1117/12.3011419
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China
Abstract
Low resolution is an important factor affecting the accuracy of face recognition. Existing face image recognition methods can not overcome the limitation of low resolution face image recognition, resulting in poor face recognition results. Therefore, an adaptive recognition method for low resolution face images based on high order singular value decomposition and DNN model is proposed. Integrating graph method is used to extract face image features, and high order singular value decomposition (HODVD) is used to separate face image features from identity information, image illumination information and image pose information. On the basis of big data face samples, deep neural network recognition model technology is introduced, and based on stable and open source tensorflow implementation, face image recognition is completed. The experimental results show that the recognition rate of the proposed method is 97.2% and the running time is 8.6s, which is effective in application and provides a reliable basis for related fields.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yingchun Du, Tingting Hou, Yongxin Zhang, Chao Wu, and Xiao Wang "Low resolution face image adaptive recognition method based on high order singular value decomposition and DNN model", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129231H (8 November 2023); https://doi.org/10.1117/12.3011419
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KEYWORDS
Facial recognition systems

Data modeling

Image resolution

Feature extraction

Education and training

Singular value decomposition

Matrices

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