Optical Coherence Tomography-Based Fingerprint Presentation Attack Detection via Multi-Tissue Features Contribution Analysis
Source abstract
Abstract
Currently, unsupervised anti-spoofing methods suffer from low accuracy and susceptibility to irrelevant factors. In order to solve these problems, this paper analyzes the contribution of different fingertip tissue structures to anti-spoofing tasks and proposes an unsupervised anti-spoofing method based on the weighted contribution of tissue structures. Unsupervised OCT anti-spoofing methods suffer from weak robustness and lack comprehensive exploration of sub-dermal structure features. The proposed method introduces quantified weights of fingertip tissue contributions with self-attention through the Shapley value. The module can amplify crucial fingertip features and extracts more key fingertip information, thereby improving the anti-spoofing performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Biometric Identification and Security · Forensic Fingerprint Detection Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Funding
National Natural Science Foundation of China · Medical Health Science and Technology Project of Zhejiang Province
References (51)
Show source-provided references
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