A Lightweight Multimodal Footprint Recognition Network Based on Progressive Multi-Granularity Feature Fusion.
| Title: | A Lightweight Multimodal Footprint Recognition Network Based on Progressive Multi-Granularity Feature Fusion. |
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| Authors: | Cao, Ruike1 (AUTHOR) p02014263@stu.ahu.edu.cn, Li, Luowei2 (AUTHOR) p02014007@stu.ahu.edu.cn, Zhang, Yan1 (AUTHOR) zhangyan@ahu.edu.cn, Wu, Jun1 (AUTHOR) junwu@ahu.edu.cn, Zhao, Xinyu1 (AUTHOR) p01914206@stu.ahu.edu.cn |
| Source: | International Journal of Pattern Recognition & Artificial Intelligence. Nov2023, Vol. 37 Issue 14, p1-17. 17p. |
| Subject Terms: | Footprints, Image recognition (Computer vision), Feature extraction, Muscle strength, Human body |
| Abstract: | The main differences in images of footprints are the proportion of the parts of foot and the distribution of pressure, which can be considered as fine-grained image classification. Moreover, the deviation of human body weight and muscle strength increases the difficulty of identifying the left and right feet. While using a fine-grained image classification network to solve the footprint image classification problem is certainly a feasible approach, the number of parameters in a fine-grained image classification network is generally large, and therefore we would like to build a lightweight classification network that is suitable for several small footprint datasets. In this paper, a multimodal footprint recognition algorithm based on progressive multi-granularity feature fusion is proposed. First, the shallow dense connection network is used to extract features. The feature extraction ability of the model is improved with the help of channel splicing and feature multiplexing. Second, to learn footprint images of different granularities, the progressive training strategy and puzzle scrambler are applied to the model. Finally, factorized bilinear coding can aggregate local features to obtain more discriminative global representation features. Experiments show that our network achieves comparable classification accuracy to some fine-grained image classification models (PMG, MSEC) on the complete pressure footprint dataset, but the number of parameters in our network is greatly reduced. Meanwhile, our network also achieves good classification results on several other footprint datasets, which demonstrates the effectiveness of our network. At the same time, an ablation experiment was carried out to verify the effectiveness of the progressive strategy and the factorized bilinear coding. [ABSTRACT FROM AUTHOR] |
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| Items | – Name: Title Label: Title Group: Ti Data: A Lightweight Multimodal Footprint Recognition Network Based on Progressive Multi-Granularity Feature Fusion. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cao%2C+Ruike%22">Cao, Ruike</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> p02014263@stu.ahu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Luowei%22">Li, Luowei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> p02014007@stu.ahu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yan%22">Zhang, Yan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhangyan@ahu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Jun%22">Wu, Jun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> junwu@ahu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Xinyu%22">Zhao, Xinyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> p01914206@stu.ahu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Pattern+Recognition+%26+Artificial+Intelligence%22">International Journal of Pattern Recognition & Artificial Intelligence</searchLink>. Nov2023, Vol. 37 Issue 14, p1-17. 17p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Footprints%22">Footprints</searchLink><br /><searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Muscle+strength%22">Muscle strength</searchLink><br /><searchLink fieldCode="DE" term="%22Human+body%22">Human body</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The main differences in images of footprints are the proportion of the parts of foot and the distribution of pressure, which can be considered as fine-grained image classification. Moreover, the deviation of human body weight and muscle strength increases the difficulty of identifying the left and right feet. While using a fine-grained image classification network to solve the footprint image classification problem is certainly a feasible approach, the number of parameters in a fine-grained image classification network is generally large, and therefore we would like to build a lightweight classification network that is suitable for several small footprint datasets. In this paper, a multimodal footprint recognition algorithm based on progressive multi-granularity feature fusion is proposed. First, the shallow dense connection network is used to extract features. The feature extraction ability of the model is improved with the help of channel splicing and feature multiplexing. Second, to learn footprint images of different granularities, the progressive training strategy and puzzle scrambler are applied to the model. Finally, factorized bilinear coding can aggregate local features to obtain more discriminative global representation features. Experiments show that our network achieves comparable classification accuracy to some fine-grained image classification models (PMG, MSEC) on the complete pressure footprint dataset, but the number of parameters in our network is greatly reduced. Meanwhile, our network also achieves good classification results on several other footprint datasets, which demonstrates the effectiveness of our network. At the same time, an ablation experiment was carried out to verify the effectiveness of the progressive strategy and the factorized bilinear coding. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0218001423570124 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Footprints Type: general – SubjectFull: Image recognition (Computer vision) Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Muscle strength Type: general – SubjectFull: Human body Type: general Titles: – TitleFull: A Lightweight Multimodal Footprint Recognition Network Based on Progressive Multi-Granularity Feature Fusion. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cao, Ruike – PersonEntity: Name: NameFull: Li, Luowei – PersonEntity: Name: NameFull: Zhang, Yan – PersonEntity: Name: NameFull: Wu, Jun – PersonEntity: Name: NameFull: Zhao, Xinyu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 02180014 Numbering: – Type: volume Value: 37 – Type: issue Value: 14 Titles: – TitleFull: International Journal of Pattern Recognition & Artificial Intelligence Type: main |
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