A Fine-grained Biometric Image Recognition Method Based on Transformer
Authors: 
Ying Xing, Yu Guan, Bin Yang, Jingze Liu, Ze Chen, Man ZhangAuthors Info & Claims
Pages 19 - 23
Abstract
Fine-grained biometric image recognition aims to achieve classification of subclasses by processing detailed features, which is still a critical problem to be solved in computing due to the small differences between subclasses. In recent years, Transformer model, which was originally used in natural language processing, has been applied to computer vision. The transformer model splits the image into patches and calculates the weights between different parts to obtain a better feature representation. In this paper, we propose a model of transformer for fine-grained biometric image recognition. Specifically, in the process of patch coding by the model, our model generates corresponding weights for all patches, and saves corresponding attention scores. To verify the effectiveness of our method, we conducted experiments on the CUB-200-2011 and Stanford Dog datasets.
References
[1]
Van Horn G, Branson S, Farrell R, Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 595-604.
[2]
Wah C, Branson S, Welinder P, The caltech-ucsd birds-200-2011 dataset[J]. 2011.
[3]
Khosla A, Jayadevaprakash N, Yao B, Novel dataset for fine-grained image categorization: Stanford dogs[C]//Proc. CVPR workshop on fine-grained visual categorization (FGVC). Citeseer, 2011, 2(1).
[4]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.
[5]
Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Advances in neural information processing systems, 2012, 25.
[6]
He K, Zhang X, Ren S, Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.
[7]
Ding Y, Zhou Y, Zhu Y, Selective sparse sampling for fine-grained image recognition[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 6599-6608.
[8]
Liu C, Xie H, Zha Z J, Filtration and distillation: Enhancing region attention for fine-grained visual categorization[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(07): 11555-11562.
[9]
Huang X, Wang X, Lv W, PP-YOLOv2: A practical object detector[J]. arXiv preprint arXiv:2104.10419, 2021.
[10]
Ren S, He K, Girshick R, Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Advances in neural information processing systems, 2015, 28.
[11]
Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7132-7141.
[12]
Li X, Wang W, Hu X, Selective kernel networks[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 510-519.
[13]
Dosovitskiy A, Beyer L, Kolesnikov A, An image is worth 16x16 words: Transformers for image recognition at scale[J]. arXiv preprint arXiv:2010.11929, 2020.
[14]
Liu Z, Lin Y, Cao Y, Swin transformer: Hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 10012-10022.
[15]
Carion N, Massa F, Synnaeve G, End-to-end object detection with transformers[C]//European conference on computer vision. Cham: Springer International Publishing, 2020: 213-229.
[16]
Chen J, Lu Y, Yu Q, Transunet: Transformers make strong encoders for medical image segmentation[J]. arXiv preprint arXiv:2102.04306, 2021.
[17]
Zheng S, Lu J, Zhao H, Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 6881-6890.
[18]
Vaswani A, Shazeer N, Parmar N, Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.
[19]
Wang X, Girshick R, Gupta A, Non-local neural networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7794-7803.
[20]
Jaderberg M, Simonyan K, Zisserman A. Spatial transformer networks[J]. Advances in neural information processing systems, 2015, 28.
[21]
Wang Q, Wu B, Zhu P, ECA-Net: Efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 11534-11542.
[22]
Woo S, Park J, Lee J Y, Cbam: Convolutional block attention module[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 3-19.
[23]
Fu J, Liu J, Tian H, Dual attention network for scene segmentation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 3146-3154.
[24]
Huang Z, Wang X, Huang L, Ccnet: Criss-cross attention for semantic segmentation[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2019: 603-612.
[25]
Wang F, Jiang M, Qian C, Residual attention network for image classification[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 3156-3164.
[26]
He S, Luo H, Wang P, Transreid: Transformer-based object re-identification[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 15013-15022.
[27]
He J, Chen J N, Liu S, Transfg: A transformer architecture for fine-grained recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(1): 852-860.
Index Terms
- A Fine-grained Biometric Image Recognition Method Based on Transformer
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Published In
December 2023
648 pages
ISBN:9798400708909
DOI:10.1145/3638884
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 23 April 2024
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ICCIP 2023
ICCIP 2023: 2023 the 9th International Conference on Communication and Information Processing
December 14 - 16, 2023
Lingshui, China
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Overall Acceptance Rate 61 of 301 submissions, 20%
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