A Line Feature Extraction Method for Finger-Knuckle-Print Verification

Kim, Joo-Young; Oh, Ki–Young; Oh, Beom Seok; Zhi-ping, Lin; Toh, Kar-Ann

doi:10.1007/s12559-018-9593-6

Cited by 23 publications

(13 citation statements)

References 45 publications

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“…Since the Wi-Fi signals can be easily affected by surrounding terrain (e.g., location of stuffs on the table), the collected Wi-Fi signal contains DC component arising from minor changes of the data acquisition process. A shifting and subtraction algorithm described in [47] has been applied to remove the DC component. Figure 4 shows an illustration of the shifting and subtraction process.…”

Section: ) Data Collection and Preprocessingmentioning

confidence: 99%

“…Among these fusion levels, the score-level fusion is among the most commonly used due to the ease of accessing scores generated by commercial matchers [50], [51]. Moreover, it is known to produce the best classification accuracy performance [47], [50], [51]. For example, simple non-learning based algorithms such as the SUM-rule, the MAX-rule and the MINrule were performed and compared in [52]- [55].…”

Section: Decision Fusionmentioning

confidence: 99%

“…For example, simple non-learning based algorithms such as the SUM-rule, the MAX-rule and the MINrule were performed and compared in [52]- [55]. Apart from the above fusion means, learning based algorithms, such as SVM [56] and TER [57], can be adopted for score level fusion [47], [58]- [60]. However, learning based fusion requires additional computational cost for training.…”

Section: Decision Fusionmentioning

confidence: 99%

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Wi-Fi Based User Identification Using In-Air Handwritten Signature

et al. 2021

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This paper conducts a feasibility study regarding the use of the Wi-Fi channel state information for user recognition based on in-air handwritten signatures. A novel system for identity recognition is thus proposed to observe for distinctive signal distortions along the propagation path for different users. The system capitalizes on the vast availability of Wi-Fi signals for signal analysis without needing additional hardware infra-structure. Since the patterns of the raw Wi-Fi signals are sensitive to the signer's location, a transfer learning has been adopted to cope with the positional variation. Specifically, features trained at one position are transferred to classify signals collected at another position via a single shot retraining. A kernel and range space projection has been adopted for the single shot retraining. Our experiments show encouraging results for the proposed system.

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Section: ) Data Collection and Preprocessingmentioning

confidence: 99%

Section: Decision Fusionmentioning

confidence: 99%

Section: Decision Fusionmentioning

confidence: 99%

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Wi-Fi Based User Identification Using In-Air Handwritten Signature

et al. 2021

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“…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%

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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“…In the matching process, a novel deep matching technique has been used. Similarly, Kim et al [22] investigated an analytic projection-based line feature projection (LFP) method for finger-knuckle-print verification. Effectively, the both horizontal and vertical line features (H-LFP and V-LFP) have been extracted.…”

Section: Introductionmentioning

confidence: 99%

Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication

Attia

Akhtar

Chahir

2020

SIViP

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The identification of individuals by their finger dorsal patterns has become a very active area of research in recent years. In this paper, we present a multimodal biometric personal identification system that combines the information extracted from the finger dorsal surface image with the major and minor knuckle pattern regions. In particular, first the features are extracted from each single region by BSIF (binarized statistical image features) technique. Then, extracted information is fused at feature level. Fusion is followed by dimensionality reduction step using PCA (principal component analysis) + LDA (linear discriminant analysis) scheme in order to improve its discriminatory power. Finally, in the matching stage, the cosine Mahalanobis distance has been employed. Experiments were conducted on publicly available database for minor and major finger knuckle images, which was collected from 503 different subjects. Reported experimental results show that feature-level fusion leads to improved performance over single modality approaches, as well as over previously proposed methods in the literature.

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A Line Feature Extraction Method for Finger-Knuckle-Print Verification

Cited by 23 publications

References 45 publications

Wi-Fi Based User Identification Using In-Air Handwritten Signature

Wi-Fi Based User Identification Using In-Air Handwritten Signature

Palm-Vein Verification Using Images From the Visible Spectrum

Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication

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