A Deep Learning Multifractal Texture Analysis Using Principal Line Extraction Approach for Palmprint Recognition System | [International Journal of Communication Networks and Information Security • 2024]

Author(s):

1. Abirami B: Affiliated with Madurai Kamaraj University, Research Scholar, Research Department of Computer Science, Sattur - 626203, Virudhunagar District, Tamilnadu, India

2. Krishnaveni K: Affiliated with Madurai Kamaraj University, Research Scholar, Research Department of Computer Science,Sattur - 626203, Virudhunagar District, Tamilnadu,India

Abstract:

To make the methodical Palmprint Recognition System (PRS), this research proposed an unique Deep Learning classifier using the palm hand's principal lines extraction approach and multifractal texture analysis approach. To reveal the efficient biometric security, a Deep Learning Multifractal Texture Analysis for Palmprint Recognition System (DLMTA-PRS) has been suggested. In DLMTA-PRS system, exact principal lines of Two-Dimensional Palmprint Region of Interest (2DPROI) image are extracted using morphological operations and an edge detection algorithm in a peculiar manner. Then, Feature values of 2DPROI image are fetched using multifractal texture analysis approach. To perform this approach, Box-counting and Gliding-Box algorithms are performed and the feature vector is created. The feature vector is classified using the proposed Convolution Neural Network (CNNNet) classifier approach to get the higher authentication security. The multispectral 2DPROI image database has been utilized for this research which is acquired from the PolyU, the Hong Kong Polytechnic University in Hong Kong. The suggested DLMTA-PRS system underwent scrutiny as well as proved the best of its by evaluating the DLMTA-PRS system using numerous criterias with the achievement of getting 99.25% accurate identification rate.

Page(s): 237-250

DOI: DOI not available

Published: Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: S1, Year: 2024

Keywords:

Morphological operation , convolution neural network , BoxCounting Algorithm , Principal lines features , Edge Detection Algorithm , Palmprint Recognition System , GlidingBox Algorithm , Multifractal Texture Analysis

References:

[1] Guennouni S.,Mansouri A.,Ahaitouf A. .2020 .Biometric Systems and Their Applications”, Visual Impairment and Blindness - What We Know and What We Have to Know. , 10 : .

[2] El-Tarhouni .2017 .Multispectral palmprint recognition using Pascal coefficients-based LBP and PHOG descriptors with random sampling. ” Neural Computing & Applications, : .

[3] Kong Adams,Wai-Kin Adams,Nov Adams .2006 .A study of identical twins' palmprints for personal verification. ” Pattern Recognition, 04(11) : 2149-56.

[4] Apolinário ,Ana C .2016 .Dental panoramic indices and fractal dimension measurements in osteogenesis imperfecta children under pamidronate treatment.” Dento maxillo facial radiology vol. , 10(4) : .

[5] Gumussoy ,Ismail ,May 2016 . .Fractal properties of the trabecular pattern of the mandible in chronic renal failure”, Dento-maxillo-facial Radiology/Dentomaxillofacial Radiology. , 20150389(doi) : .

[6] Bhowmik M. K.,Roy A.,Gogoi U. R.,Nath N.,GA N. .2017 ."Estimation of Architectural Distortion in Mammograms using Fractal Features,". 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 8533040 : 1-3.

[7] Bhowmik Mrinal,Kanti Mrinal .2017 .Estimation of Architectural Distortion in Mammograms using Fractal Features”. , 8533040 : .

[8] Filho M. N. Barros,Sobreira F.J. .2008 .Accuracy of lacunarity algorithms in texture classification of high spatial resolution images from urban areas”, In XXI congress of international society of photo grammetry and remote sensing. , : 417-422.

[9] Myint S.W. .2005 .A study of lacunarity-based texture analysis approaches to improve urban image classification”. 5 SPEC, 01 : 501-523.

[10] Allain C.,Cloitre M.,Review A,Sept A .1991 .Characterizing the lacunarity of random and deterministic fractal sets”, Physical. Optical Physics, 44(6) : 3552-58.

[11] Sarkar B. B. Chaudhuri. “,Sept B. B. Chaudhuri. “ .1992 .An efficient approach to estimate fractal dimension of textural images. ” Pattern Recognition, 25(92) : 90066-r.

[12] Dong P.,Jan P. .2000 .Test of a new lacunarity estimation method for image texture analysis. International Journal of Remote Sensing, 21(17) : 3369-73.

[13] Harte D.,Mar D. .2002 .Multifractals: theory and applications. , 39(07) : 4021-4021.

[14] Hsui ,Che-Yu ,Chia-Chun Wang,Apr Wang .2013 .Synergy between fractal dimension and lacunarity index in design of artificial habitat for alternative SCUBA diving site”. Ecological Engineering, 01 : 6-14.

[15] Kamboj ,Aman ,Nov .2020 .CED-Net: context-aware ear detection network for unconstrained images”. Pattern Analysis and Applications/Pattern Analysis & Applications, 24(2) : 779-800.

[16] Kusban ,Apr .1858 .Improvement Palmprint Recognition System by Adjusting Image Data Reference Points”. Journal of Physics. Conference Series, 012077(doi) : 1742-6596.

[17] Amrouni ,Nadia ,Dec .2022 .Contactless Palmprint Recognition Using Binarized Statistical Image Features-Based Multiresolution Analysis. ” Sensors, 9814(doi) : .

[18] Kumar H.,Kumar S. Ashok,Jan S. Ashok .2023 .Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm”. E3S Web of Conferences, 04027(doi) : .

[19] Poonia ,Pawan K.,Ajmera K. .2024 .Robust Palm-print Recognition Using Multiresolution Texture Patterns with Artificial Neural Network. , : .

Citations

Downloads

Views