A Contactless Rotation-Invariant Palm Vein Recognition System

Jhinn, Wee Lorn; Goh, Michael Kah Ong; Lau, Siong Hoe; Connie, Tee

doi:10.1166/asl.2018.10704

Cited by 3 publications

(3 citation statements)

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“…Traditional palm vein recognition algorithms mainly used physical patterns including minutiae points, ridges, and texture for matching. For instance, winner‐take‐all hashing (WTA) [35], random sample consensus (RANSAC) [36], and adaptive 2D Gabor filter [37] were all applied to improve the representation performance. Sometimes, the palm vein image for recognition is not always clear, and irregular shading and saturated areas may appear, leading to prolonged processing time.…”

Section: Related Workmentioning

confidence: 99%

A deep biometric hash learning framework for three advanced hand‐based biometrics

Shao

Zhong

2021

IET Biometrics

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Hand‐based biometrics has undergone extensive research in recent decades. Besides fingerprint, which is commonly used in personal authentication, there are other three advanced hand‐based biometrics that are further researched worldwide, such as palmprint, palm vein, and dorsal hand vein. However, the academics mainly focus on their unimodal or multimodal recognitions, and few researchers conduct comprehensive comparisons for them to guide practical applications, that is, to assess which one is the suitable biometric modality. Inspired by deep hashing network (DHN) and transfer learning, we propose a deep biometric hash learning (DBHL) framework to uniformly analyse and deal with these three biometrics. An end‐to‐end network is involved to convert images into binary codes. Pre‐trained network is employed for fine‐tuning, and the hamming distance is adopted to measure the similarity between the codes of query and registration images. Through experiments on benchmarks, equal error rate (EER) and the maximum or minimum distance of genuines or imposters are obtained as performance metrics. In experiments, EERs of palmprint and palm vein recognitions reach 0%, and EER of dorsal hand vein recognition is as low as 0.196%. It shows that the DBHL framework is effective to construct hand‐based biometric systems. Furthermore, a plain comparison among them shows that palmprint is more suitable in practical applications.

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Section: Related Workmentioning

confidence: 99%

A deep biometric hash learning framework for three advanced hand‐based biometrics

Shao

Zhong

2021

IET Biometrics

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“…Of the available identification methods, those based on palm veins patterns have demonstrated remarkable achievements [1–5]. These methods build on the fact that the geometrical arrangements of palm veins in people vary widely.…”

Section: Introductionmentioning

confidence: 99%

“…Jhinn et al . proposed a rotation‐invariant method for detecting a target region in a palm image irrespective of its orientation [1]. This method can be integrated into the PVR system to create flexibility when placing a palm on the system.…”

Section: Introductionmentioning

confidence: 99%

Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

et al. 2020

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Palm vein recognition (PVR) refers to the contactless biometric identification method that uses palm vein patterns to confirm the identity of a person. Compared with other methods, PVR has received a wide attention because it provides more secure results. The veins, being located inside the human body, make PVR robust against tempering and changes in morphology of body features. Most PVR systems integrate four stages: image acquisition, pre-processing, feature extraction, and decision. The first two stages determine accuracy of the final identification results. Focusing on the pre-processing component, we discovered that the available approaches fail to generate more informative vein patterns by simultaneously suppressing noise and blur, and also by recovering semantically useful features (edges, contours, and lines) from the acquired images. This weakness calls for sophisticated acquisition devices that make PVR systems costly. In this work, we have proposed multiframe super-resolution (MSR) as a pre-processing stage to improve performance of the traditional PVR systems. MSR exploits information from multiple images of the same scene to reconstruct a high-resolution image. This technique signals the possibility of using inexpensive low-resolution imaging devices demanded by the traditional PVR systems. Experiments show that our method outperforms most classical methods.

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