Palmprint identification using hausdorff distance

Li, Fang; Leung, M.K.H.; Yu, Xiaozhou

doi:10.1109/biocas.2004.1454123

Cited by 7 publications

(1 citation statement)

References 6 publications

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“…Creases are minute features that spread over the whole palm, just as the ridges do in a fingerprint. Line-based methods either develop novel edge detectors or use off-the-shelf ones to find palm-lines [16][17][18]. Shu et al [19] proposed a way to extract features based on principle lines, which uses 12 line detection operators to detect palmprint lines in all directions and approximate them with straight line segments.…”

Section: Palmprint/palmvein Recognition Methodsmentioning

confidence: 99%

Palmprint and Palmvein Recognition Based on DCNN and A New Large-Scale Contactless Palmvein Dataset

et al. 2018

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Among the members of biometric identifiers, the palmprint and the palmvein have received significant attention due to their stability, uniqueness, and non-intrusiveness. In this paper, we investigate the problem of palmprint/palmvein recognition and propose a Deep Convolutional Neural Network (DCNN) based scheme, namely PalmR CNN (short for palmprint/palmvein recognition using CNNs). The effectiveness and efficiency of PalmR CNN have been verified through extensive experiments conducted on benchmark datasets. In addition, though substantial effort has been devoted to palmvein recognition, it is still quite difficult for the researchers to know the potential discriminating capability of the contactless palmvein. One of the root reasons is that a large-scale and publicly available dataset comprising high-quality, contactless palmvein images is still lacking. To this end, a user-friendly acquisition device for collecting high quality contactless palmvein images is at first designed and developed in this work. Then, a large-scale palmvein image dataset is established, comprising 12,000 images acquired from 600 different palms in two separate collection sessions. The collected dataset now is publicly available.

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Section: Palmprint/palmvein Recognition Methodsmentioning

confidence: 99%

Palmprint and Palmvein Recognition Based on DCNN and A New Large-Scale Contactless Palmvein Dataset

et al. 2018

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Online fortune telling system using biometrics feature

Leung

Khanna

et al. 2007

2007 IEEE International Conference on Systems, Man and Cybernetics

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Palmistry is an ancient type of art and is trusted by generations of people. Our system aims at introducing this ancient science to the modern technology. Instead of traditional face-to-face palm reading, our online fortune telling system is where the users submit their hand images online and get the predictions immediately. The major line patterns in palmistry are summarized to become the models for online fortune telling through palm reading. The images are converted to Hough space to extract principal lines. This is a novel approach to the application of the Hough transform for feature extraction of palmprint. A principal line detection mechanism in transformed space is also proposed based on a flooding process, which is motivated by Rainfalling Watershed segmentation algorithm. The local neighbourhood information of principal lines is used as a means to measure similarity as well. The experiment results show that we achieve low time complexity with high accuracy.

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