Competitive Coding Scheme based on 2D Log-Gabor filter for Palm Vein Recognition

Boubchir, Larbi; Aberni, Yassir; Daachi, B.

doi:10.1109/ahs.2018.8541451

Cited by 5 publications

(7 citation statements)

References 14 publications

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“…where 0 0 0 and I I I denote respectively the zero and the identity matrices. A projection operation on an image X X X by k pixels of shift to the horizontal direction is obtained by [11]- [14], [16]- [20], [22], [52], [53] (c) Nil Palmprint (d) [23], [24], [27]- [30] (e) [54] (f) [25], [26], [31]- [36] Palm-vein + palmprint (g) [55] (h) [15], [48], [56]- [58] (i) [50] a : The conference presentation [21] was a preliminary work of the current proposal. b : The current manuscript has lower than 10% of content overlap with that of [21].…”

Section: B Shift Matrixmentioning

confidence: 99%

“…As [21] and [55] are the only competing works on RGB palm-vein, we include several handcrafted NIR palm-vein methods [11]- [14], [16]- [20], [22] in our experiments. While noting the remarkable performance of CNN networks on the IR and NIR palm-vein images (see e.g., [47], [69]), we shall focus on establishing the feasibility and the baseline of RGB palm-vein relative to the handcrafted NIR methods in the literature.…”

Section: A Databasesmentioning

confidence: 99%

“…For testing using the CASIA-M database, there are 1,800 genuine matches obtained from 200 (100 subjects × 2 palms) × 3 (images per palm from Session 1 gallery) × 3 (images per palm from Session 2 probe), and 358,200 impostor matches based on 200 (palms) × 199 (other palms) × 3 (images per palm from Session 1 gallery) × 3 (images per palm from Session 2 probe). All the state-of-the-art algorithms [11]- [14], [16]- [20], [22] are implemented and evaluated under the same protocol with the proposed method in Experiments II and III.…”

Section: ) Evaluation Protocolmentioning

confidence: 99%

“…This is because the hand-based biometrics are highly practical for daily applications in view of their feature stability, ease of collection and usage convenience [4]. The hand biometrics consist of dorsal-vein [5]- [7], finger-vein [8]- [10], palmvein [11]- [22], palmprint [23]- [36], fingerprint [37]- [39], inner-knuckle print [40]- [42], and hand-geometry [43]- [45]. Fingerprint, palmprint and inner-knuckle-print are widely utilized for user authentication, but these modalities are sensitive to external imaging conditions such as illumination change and noise on the hand (e.g., moisture, dust etc.).…”

Section: Introductionmentioning

confidence: 99%

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Palm-Vein Verification Using Images From the Visible Spectrum

Cho

Kim

et al. 2021

IEEE Access

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In this paper, we investigate into utilization of images from the visible light (RGB) spectrum for identity verification based on the palm-veins. This is differentiated from the commonly utilized Nearinfrared (NIR) images for palm-vein feature extraction. Our goal is to explore into the often omitted palm-vein information from the RGB palm images considering the vast deployment of the RGB cameras. Essentially, the vein line features are extracted at various scales based on an efficient difference image projection. The extracted features from the gallery and the probe images are matched based on a modified Hamming distance measure. The resultant similarity scores are finally fused at score level for accuracy enhancement. Experiments are conducted on two public multi-spectral palm databases. The results show encouraging matching accuracy and computational efficiency of the proposed method which extracts the palm-vein utilizing only the visible spectrum. The outcome of this study can be deployed as a standalone biometric or as part of a multibiometric system for secured authentication.

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Section: B Shift Matrixmentioning

confidence: 99%

Section: A Databasesmentioning

confidence: 99%

Section: ) Evaluation Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Palm-Vein Verification Using Images From the Visible Spectrum

Cho

Kim

et al. 2021

IEEE Access

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“…The texture-based approaches use various statistical texture feature [44], such as local derivative pattern (LDP) [45,46], local binary pattern (LBP) [12,47,48] and its variant [18,[49][50][51][52] or structure texture feature, such as Gabor and its variant [11,14,[53][54][55][56][57][58] from palm vein images. These methods can depict a grey histogram distribution of the vein image, but they lose positional information on the vein texture.…”

Section: Feature Extraction and Matchingmentioning

confidence: 99%

Review of palm vein recognition

Elliott

Lin

et al. 2019

IET biom.

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As a new research focus in biometrics, palm vein recognition has attracted people's attention because of high security, liveness-detection, user acceptability and convenience. Palm vein recognition exhibits high security, as it uses the network of blood vessels underneath palm skin for recognition. As palm vein is interior biological information of the body, vein patterns are much harder for intruders to copy compared to other biometric features. Palm vein is mostly invisible to the human eyes; they are commonly captured under near-infrared light. Under its natural state, the veins of the palm are concealed naturally and, for most of time people's hands are in the gesture of half fist. This study presents a comprehensive overview of recent research progress of palm vein recognition, from the basic background knowledge to data acquisition, public database, preprocessing, feature extraction and matching. In addition, the study focuses on palm vein-related fusion. Ultimately, the authors discuss the challenges and future perspectives in palm vein recognition for further works.

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