Towards Palmprint Verification On Smartphones
Yingyi Zhang, Lin Zhang, Ruixin Zhang, Shaoxin Li, Jilin Li, Feiyue, Huang

TL;DR
This paper introduces a new large-scale palmprint dataset for smartphones and develops a deep learning-based verification system, demonstrating promising results and filling a significant research gap in mobile biometric authentication.
Contribution
The paper presents the largest palmprint dataset for smartphones and a novel DCNN-based verification system, advancing research in mobile palmprint recognition.
Findings
The MPD dataset contains 16,000 palm images from 200 subjects.
DeepMPV+ achieves high accuracy in palmprint verification.
The dataset and code are publicly available for reproducibility.
Abstract
With the rapid development of mobile devices, smartphones have gradually become an indispensable part of people's lives. Meanwhile, biometric authentication has been corroborated to be an effective method for establishing a person's identity with high confidence. Hence, recently, biometric technologies for smartphones have also become increasingly sophisticated and popular. But it is noteworthy that the application potential of palmprints for smartphones is seriously underestimated. Studies in the past two decades have shown that palmprints have outstanding merits in uniqueness and permanence, and have high user acceptance. However, currently, studies specializing in palmprint verification for smartphones are still quite sporadic, especially when compared to face- or fingerprint-oriented ones. In this paper, aiming to fill the aforementioned research gap, we conducted a thorough study…
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Taxonomy
TopicsBiometric Identification and Security · Face recognition and analysis · User Authentication and Security Systems
MethodsDiffusion-Convolutional Neural Networks
