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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.16202 |
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| _version_ | 1866929287266828288 |
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| author | Sharma, Geetanjali Jaswal, Gaurav Nigam, Aditya Ramachandra, Raghavendra |
| author_facet | Sharma, Geetanjali Jaswal, Gaurav Nigam, Aditya Ramachandra, Raghavendra |
| contents | Biometric authentication, which utilizes contactless features, such as forehead patterns, has become increasingly important for identity verification and access management. The proposed method is based on learning a 3D spatio-spatial temporal convolution to create detailed pictures of forehead patterns. We introduce a new CNN model called the Forehead Spatio-Spatial Temporal Network (FH-SSTNet), which utilizes a 3D CNN architecture with triplet loss to capture distinguishing features. We enhance the model's discrimination capability using Arcloss in the network's head. Experimentation on the Forehead Creases version 1 (FH-V1) dataset, containing 247 unique subjects, demonstrates the superior performance of FH-SSTNet compared to existing methods and pre-trained CNNs like ResNet50, especially for forehead-based user verification. The results demonstrate the superior performance of FH-SSTNet for forehead-based user verification, confirming its effectiveness in identity authentication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16202 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FH-SSTNet: Forehead Creases based User Verification using Spatio-Spatial Temporal Network Sharma, Geetanjali Jaswal, Gaurav Nigam, Aditya Ramachandra, Raghavendra Computer Vision and Pattern Recognition Biometric authentication, which utilizes contactless features, such as forehead patterns, has become increasingly important for identity verification and access management. The proposed method is based on learning a 3D spatio-spatial temporal convolution to create detailed pictures of forehead patterns. We introduce a new CNN model called the Forehead Spatio-Spatial Temporal Network (FH-SSTNet), which utilizes a 3D CNN architecture with triplet loss to capture distinguishing features. We enhance the model's discrimination capability using Arcloss in the network's head. Experimentation on the Forehead Creases version 1 (FH-V1) dataset, containing 247 unique subjects, demonstrates the superior performance of FH-SSTNet compared to existing methods and pre-trained CNNs like ResNet50, especially for forehead-based user verification. The results demonstrate the superior performance of FH-SSTNet for forehead-based user verification, confirming its effectiveness in identity authentication. |
| title | FH-SSTNet: Forehead Creases based User Verification using Spatio-Spatial Temporal Network |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.16202 |