Characterization of Palmprints by Wavelet Signatures via Directional Context Modeling

Zhang, Lei

doi:10.1109/tsmcb.2004.824521

Cited by 197 publications

(87 citation statements)

References 29 publications

(53 reference statements)

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“…In palmprint recognition [4][5][6], the features used for matching are the principal lines and wrinkles. Actually, the outer surfaces of finger joints have even more obvious line features than the palm surface, while they have much smaller area than the palm surface.…”

Section: Introductionmentioning

confidence: 99%

Finger-knuckle-print: A new biometric identifier

Zhang

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

139

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This paper presents a new biometric identifier, namely finger-knuckle-print (FKP), for personal identity authentication. First a specific data acquisition device is constructed to capture the FKP images, and then an efficient FKP recognition algorithm is presented to process the acquired data. The local convex direction map of the FKP image is extracted, based on which a coordinate system is defined to align the images and a region of interest (ROI) is cropped for feature extraction. A competitive coding scheme, which uses 2D Gabor filters to extract the image local orientation information, is employed to extract and represent the FKP features. When matching, the angular distance is used to measure the similarity between two competitive code maps. An FKP database was established to examine the performance of the proposed system, and the experimental results demonstrated the efficiency and effectiveness of this new biometric characteristic.

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Section: Introductionmentioning

confidence: 99%

Finger-knuckle-print: A new biometric identifier

Zhang

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

139

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“…Line segments [5] and lines features [6] are also employed to extract parincipal lines. In [7] the authors performed a compact method to detect principle lines of palmprint is proposed with consecutive filtering operations related to gradient and morphological operators.…”

Section: State Of the Artmentioning

confidence: 99%

Palmprint principal lines extraction

Bruno

Carminetti

Gentile

et al. 2014

2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS) Proceedings

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Abstract-The palmprint recognition has become a focus in biological recognition and image processing fields. In this process, the features extraction (with particular attention to palmprint principal line extraction) is especially important. Although a lot of work has been reported, the representation of palmprint is still an open issue. In this paper we propose a simple, efficient, and accurate palmprint principal lines extraction method. Our approach consists of six simple steps: normalization, median filtering, average filters along four prefixed directions, grayscale bottom-hat filtering, combination of bottom-hat filtering, binarization and post processing. The contribution of our work is a new method for palmprint principal lines detection and a new dataset of hand labeled principal lines images (that we use as ground truth in the experiments). Preliminary experimental results showed good performance in terms of accuracy with respect to three methods of the state of the art.

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“…Various palmprint recognition approaches have been proposed in the literatures [1][2][3][4][5][6][7][8][9][10][11][12]. Almost all available algorithms involve thousands of large-size convolutions or filtering, i.e.…”

Section: Introductionmentioning

confidence: 99%

Embedded Palmprint Recognition System on Mobile Devices

Han¹,

Tan²,

Sun³

et al.

Advances in Biometrics

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Abstract. There are increasing requirements for mobile personal identification, e.g. to protect identity theft in wireless applications. Based on built-in cameras of mobile devices, palmprint images may be captured and analyzed for individual authentication. However, current available palmprint recognition methods are not suitable for real-time implementations due to the limited computational resources of handheld devices, such as PDA or mobile phones. To solve this problem, in this paper, we propose a sum-difference ordinal filter to extract discriminative features of palmprint using only +/-operations on image intensities. It takes less than 200 ms for our algorithm to verify the identity of a palmprint image on a HP iPAQ PDA, about 1/10 of state-of-the-art methods ' complexity, while this approach also achieves high accuracy on the PolyU palmprint database. Thanks to the efficient palmprint feature encoding scheme, we develop a real-time embedded palmprint recognition system, working on the HP PDA.

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Characterization of Palmprints by Wavelet Signatures via Directional Context Modeling

Cited by 197 publications

References 29 publications

Finger-knuckle-print: A new biometric identifier

Finger-knuckle-print: A new biometric identifier

Palmprint principal lines extraction

Embedded Palmprint Recognition System on Mobile Devices

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