Summary of Cross-spectral Vision Transformer For Biometric Authentication Using Forehead Subcutaneous Vein Pattern and Periocular Pattern, by Arun K. Sharma et al.
Cross-Spectral Vision Transformer for Biometric Authentication using Forehead Subcutaneous Vein Pattern and Periocular Pattern
by Arun K. Sharma, Shubhobrata Bhattacharya, Motahar Reza, Bishakh Bhattacharya
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel lightweight cross-spectral vision transformer (CS-ViT) is proposed for biometric authentication using forehead subcutaneous vein patterns and periocular patterns. The CS-ViT framework comprises a dual-channel architecture that captures inter-dependencies in relative spectral patterns, with each channel featuring a Phase-Only Correlation Cross-Spectral Attention (POC-CSA). This approach is robust against resolution/intensity variations and illumination changes, assuming both biometric traits are from the same person. The model is suitable for edge device deployment and achieves a remarkable classification accuracy of 98.8% on the Forehead Subcutaneous Vein Pattern and Periocular Biometric Pattern (FSVP-PBP) database, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to identify people using patterns on their forehead and around their eyes. The traditional ways of doing this have some problems, like not working well with face masks or being messy. This new method uses a special kind of computer program that can look at pictures from different angles and lighting conditions. It’s very good at recognizing people and works even when they are wearing face masks. |
Keywords
» Artificial intelligence » Attention » Classification » Vision transformer » Vit
