Palmprint verification based on 2D – Gabor wavelet and pulse-coupled neural network

Wang, Xuan; Li, Lei; Wang, Mingzhe

doi:10.1016/j.knosys.2011.10.008

Cited by 59 publications

(21 citation statements)

References 17 publications

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“…In the literature, numerous texture-based approaches for palmprint recognition have been proposed. The palmprint textures can be obtained using techniques, such as Gabor wavelets (Wang et al, 2012), Fourier transformation (Imatiaz and Fattah, 2011), cosine transformation (Imatiaz and Fattah, 2011;Dale et al, 2009), wavelet transformation (Khanna and Tamrakar, 2010) and standard deviation (SD) (Gonzalez and Woods, 2009).…”

Section: Introductionmentioning

confidence: 99%

Intramodal palmprint recognition using texture feature

Latha¹,

Prasad

2017

IJISDC

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Palmprint technology is a new branch of biometrics used to identify an individual. Palmprint has rich set of features like palm lines, wrinkles, minutiae points, texture, ridges, etc. Several line and texture extraction techniques for palmprint have been extensively studied. This paper presents an intramodal authentication system based on texture information extracted from the palmprint using the Haralick features, 2D-Gabor and 2D-log Gabor filters. An individual feature vector is computed for a palmprint using the extracted texture information of each filter type. Performance of the system using three feature types is evaluated individually. Finally, we combine the three feature types using feature level fusion to develop an intramodal palmprint recognition system. The experiments are evaluated on a standard benchmark database (PolyU Database), and the results shows that significant improvement in terms of recognition accuracy and error rates with the proposed intramodal recognition system compared to individual representations.

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Section: Introductionmentioning

confidence: 99%

Intramodal palmprint recognition using texture feature

Latha¹,

Prasad

2017

IJISDC

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“…It is clear that this RR reaches 98.25% when we use the 7 samples of each person for the gallery data and 3 samples for the probe data in the matching phase. Thus, we improved the rates of about 0.88%, 7.68%, 3.05% and 7.06%, respectively the result in [8] and the three results presented in [9].…”

Section: Comparison Studymentioning

confidence: 76%

“…The global-based approaches estimate the field of texture information analysis of the image and pattern recognition and provide the comprehensive and accurate descriptions for a best palmprint recognition, e.g. Blanket dimension [4], Local Binary Pattern [5], Eigenpalms [6], Fisherpalms [7], Gabor filters [8,9], Wavelets [10,9], Fourier transform [11], Co-occurrence Matrix [12,9] and so on. Among the previous descriptors that analyzed the palmprint texture, we find that the Co-occurrence Matrix and the Gabor filters kernels are commonly used for both spatial texture information and variability of texture pattern of palmprints in the image processing fields.…”

Section: Previous Workmentioning

confidence: 99%

Biometric Palmprint identification via efficient texture features fusion

Mokni

Elleuch

Kherallah

2016

2016 International Joint Conference on Neural Networks (IJCNN)

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Recently, personal identification, which is based on the palmprint texture features analysis, has widely attracted the attention of several researchers and has gained a great popularity in the pattern recognition field. In this paper, we present a novel methodology based on texture information extracted from palmprint. Firstly, we propose an algorithm to robustly locate the Region Of Interest (ROI) of the hand. Secondly, we combine multiple descriptors to extract the palmprint texture information, which are Gray-Level Co-occurrence Matrix (GLCM) and the Gabor filters using feature level fusion. These descriptors have been broadly applied in various tasks, specifically in the image processing domain to analyze the image texture. Then, we apply the generalized discriminant analysis (GDA) to reduce the length of the feature vectors and their redundancies. Finally, we classify these final resulting features by developing the SVM method which supports several kernel functions to reach a best recognition rate. We have conducted extensive experiments on the "CASIA-Palmprint" and "PolyU-palmprint" datasets. The obtained results of the proposed approach provide promising results compared to other well-known state-of-the-art approaches.

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“…In this study, first level of decomposition is used for 2D signals, the 2D DWT is to be used [15]. Our discussion focuses on Wavelet packets (WP) for images [59,22,4]. These images are represented as an m x n gray scale matrix I[i, j] where each element of the matrix represents the intensity of a pixel.…”

Section: Dwt-based Featuresmentioning

confidence: 99%