Summary of Ifvit: Interpretable Fixed-length Representation For Fingerprint Matching Via Vision Transformer, by Yuhang Qiu et al.
IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer
by Yuhang Qiu, Honghui Chen, Xingbo Dong, Zheng Lin, Iman Yi Liao, Massimo Tistarelli, Zhe Jin
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed Interpretable Fixed-length Representation for Fingerprint Matching via Vision Transformer (IFViT) network consists of two primary modules. The first module establishes a Vision Transformer (ViT)-based Siamese Network to capture long-range dependencies and the global context in fingerprint pairs, providing interpretable dense pixel-wise correspondences of feature points. The second module takes into account both local and global representations of the aligned fingerprint pair to achieve an interpretable fixed-length representation extraction and matching. Experimental results on diverse publicly available fingerprint databases demonstrate that the proposed framework exhibits superior performance on dense registration and matching while promoting interpretability in deep fixed-length representations-based fingerprint matching. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to make fingerprint matching more understandable by creating a special network called IFViT. It’s like having a map that shows exactly where fingerprints match up, making it easier to understand why some fingerprints are similar or different. The new approach does this by using something called Vision Transformers, which help computers see patterns in images. This makes the whole process of matching and comparing fingerprints more transparent and accurate. |
Keywords
» Artificial intelligence » Siamese network » Vision transformer » Vit
