##plugins.themes.bootstrap3.article.main##

  •   M. M. Ata

  •   K. M. Elgamily

  •   M. A. Mohamed

Abstract

The presented paper proposes an algorithm for palmprint recognition using seven different machine learning algorithms. First of all, we have proposed a region of interest (ROI) extraction methodology which is a two key points technique. Secondly, we have performed some image enhancement techniques such as edge detection and morphological operations in order to make the ROI image more suitable for the Hough transform. In addition, we have applied the Hough transform in order to extract all the possible principle lines on the ROI images. We have extracted the most salient morphological features of those lines; slope and length. Furthermore, we have applied the invariant moments algorithm in order to produce 7 appropriate hues of interest. Finally, after performing a complete hybrid feature vectors, we have applied different machine learning algorithms in order to recognize palmprints effectively. Recognition accuracy have been tested by calculating precision, sensitivity, specificity, accuracy, dice, Jaccard coefficients, correlation coefficients, and training time. Seven different supervised machine learning algorithms have been implemented and utilized. The effect of forming the proposed hybrid feature vectors between Hough transform and Invariant moment have been utilized and tested. Experimental results show that the feed forward neural network with back propagation has achieved about 99.99% recognition accuracy among all tested machine learning techniques.

Keywords: feature extraction, Hough transform, invariant moments, neural networks, ROI extraction

References

M. Trikoš, I. Tot, J. Baj?eti?, K. Lalovi?, B. Jovanovi?, and D. Bogi?evi?, "Biometric Security Standardization." 2019 Zooming Innovation in Consumer Technologies Conference (ZINC), IEEE, pp. 17-20, 2019. ?

P. Drozdowski, C. Rathgeb, A. Dantcheva, N. Damer, and C. Busch, "Demographic bias in biometrics: A survey on an emerging challenge." IEEE Transactions on Technology and Society, vol. 1, no. 2, pp. 89-103, 2020.

G. Brostoff, "How AI and biometrics are driving next-generation authentication." Biometric Technology Today, vol. 2019, no .6, pp. 7-9, 2019. ?

D. Zhong, X. Du, and K. Zhong, "Decade progress of palmprint recognition: A brief survey." Neurocomputing, vol. 328, pp. 16-28, 2019.

M. M. Ali, P. L. Yannawar, and A. T. Gaikwad, "Multi-algorithm of palmprint recognition system based on fusion of local binary pattern and two-dimensional locality preserving projection." Procedia computer science, vol. 115, pp. 482-492, 2017.

N. Charfi, H. Trichili, A. M. Alimi, et al., "Bimodal biometric system for hand shape and palmprint recognition based on SIFT sparse representation." Multimedia Tools and Applications, vol. 76, no. 20, pp. 20457-20482, 2017.

J. Cheng, Q. Sun, J. Zhang, and Q. Zhang, "Supervised hashing with deep convolutional features for palmprint recognition.", Chinese Conference on Biometric Recognition, Springer, Cham, vol 10568, pp. 259-268, 2017. ?

J. Cui, J. Wen, and Z. Fan. "Appearance-based bidirectional representation for palmprint recognition." Multimedia Tools and Applications, vol. 74, no. 24, pp. 10989-11001, 2015.

Fei, Lunke, et .al, "Feature extraction methods for palmprint recognition: A survey and evaluation." IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 2, pp. 346-363, 2018.

L. Fei, B. Zhang, W. Zhang, and S. Teng , "Local apparent and latent direction extraction for palmprint recognition." Information Sciences, vol. 473, pp. 59-72, 2019.

W. Jia, B. Zhang, J. Lu, et .al, "Palmprint recognition based on complete direction representation." IEEE Transactions on Image Processing, vol. 26, no. 9, pp. 4483-4498, 2017.

L. Leng, F. Gao, Q. Chen, et al., "Palmprint recognition system on mobile devices with double-line-single-point assistance." Personal and Ubiquitous Computing, vol. 22, pp. 93-104, 2018.

S. Al-Maadeed, X. Jiang, I. Rida, et .al, "Palmprint identification using sparse and dense hybrid representation." Multimedia Tools and Applications, vol. 78, no. 5, pp. 5665-5679, 2019.

W.M. Murkowski, T. Chai, and A. W. K. Kong, "Palmprint Recognition in Uncontrolled and Uncooperative Environment." IEEE Transactions on Information Forensics and Security, vol. 15, pp. 1601-1615, 2019.

S. Raj, and M. F. Shameen, “Palm Print Recognition Using Gabor Filter and Back Propagation Neural Network." International Journal for Advance Research and Development, vol. 2, no. 6, pp. 45-51, 2017.

A. Younesi, and M. C. Amirani. "Gabor filter and texture-based features for palmprint recognition." Procedia Computer Science, vol.108, pp. 2488-2495, 2017.

X. Zhou, K. Zhou, and L. Shen, "Rotation and Translation Invariant Palmprint Recognition with Biologically Inspired Transform." IEEE Access, vol. 8, pp. 80097-80119, 2020. ?

Biometrics ideal test website (online). Available: http://biometrics.idealtest.org/dbDetailForUser.do?id=5 (Accessed : 11-june-2020).

Z. Sun, T. Tan, Y. Wang, and S. Z. Li, "Ordinal Palmprint Representation for Personal Identification.", Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 279-284, 2005.

W. Li, B. Zhang, L. Zhang, and J. Yan, "Principal line-based alignment refinement for palmprint recognition." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no. 6, pp. 1491-1499, 2012. ?

Q. Xiao, J. Lu, W. Jia, and X. Liu, "Extracting Palmprint ROI from Whole Hand Image Using Straight Line Clusters." IEEE Access, vol. 7, pp. 74327-74339, 2019. ?

R. Hedjam, H. Z. Nafchi, M. Kalacska, and M. Cheriet, "Influence of color-to-gray conversion on the performance of document image binarization: Toward a novel optimization problem." IEEE transactions on image processing, vol. 24, no. 11, pp. 3637-3651, 2015. ?

J. Kim, and S. Lee, "Extracting major lines by recruiting zero-threshold canny edge links along sobel highlights." IEEE Signal Processing Letters, vol. 22, no. 10, pp. 1689-1692, 2015. ?

K. B. Ray, and R. Misra, "Palm print recognition using hough transforms.", 2015 International Conference on Computational Intelligence and Communication Networks (CICN), IEEE, pp. 422-425, 2015. ?

Y. Li, and Z. Yang, "Progressive Probabilistic Hough Transform Based Nighttime Lane Line Detection for Micro-Traffic Road." 2018 IEEE 8th Annual International. Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, pp. 1276-1281, 2018. ?

Wu, Zhuang, et .al, "Application of image retrieval based on convolutional neural networks and Hu invariant moment algorithm in computer telecommunications." Computer Communications, vol. 150, pp. 729-738, 2020. ?

L. Chen, and C. Li, "Invariant moment features for fingerprint recognition." 2013 10th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, pp. 91-94, 2013. ?

F. Zhu, Z. Ma, X. Li, et al. "Image-text dual neural network with decision strategy for small-sample image classification.” Neurocomputing, vol. 328, pp. 182-188, 2019. ?

J. Gola, J. Webel, D. Britz, et .al, "Objective microstructure classification by support vector machine (SVM) using a combination of morphological parameters and textural features for low carbon steels." Computational Materials Science, vol 160, pp. 186-196, 2019. ?

M. Franco, A. García, and F. Cataluña, "A naive Bayes strategy for classifying customer satisfaction: A study based on online reviews of hospitality services." Journal of Business Research, vol. 101, pp. 499-506, 2019. ?

M. P. Vaishnnave, K. S. Devi, P. Srinivasan, and G. A. P. Jothi, "Detection and Classification of Groundnut Leaf Diseases using KNN classifier." 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, pp. 1-5, 2019.

J. L. Speiser, M. E. Millar, J. Tooze, E. Pl, "A comparison of random forest variable selection methods for classification prediction modeling." Expert Systems with Applications, vol. 134, pp. 93-101, 2019. ??

A. B. Møller, B. V. Iversen, A. Beucher, and M. H. Greve, "Prediction of soil drainage classes in Denmark by means of decision tree classification. " Geoderma, vol. 352, pp. 314-329, 2019. ?

S. Fletcher, and M. Z. Islam, "Decision tree classification with differential privacy: A survey." ACM Computing Surveys (CSUR), vol. 52, no. 4, pp. 1-33, 2019.

Y. Zhang, M. Ni, C. Zhang, et .al, "Research and Application of AdaBoost Algorithm Based on SVM." 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), IEEE, pp. 662-666, 2019. ?

M. Vania, D. Mureja, and D. Lee, "Automatic spine segmentation from CT images using convolutional neural network via redundant generation of class labels." Journal of Computational Design and Engineering, vol. 6, no. 2, pp. 224-232, 2019.

D. S. Prabha and J. S.Kumar, "Performance evaluation of image segmentation using objective methods. ", Indian J. Sci. Technol., vol. 9, no. 8, pp. 1-8, 2016.

Downloads

Download data is not yet available.

##plugins.themes.bootstrap3.article.details##

How to Cite
[1]
Ata, M., Elgamily, K. and Mohamed, M. 2020. Toward Palmprint Recognition Methodology Based Machine Learning Techniques. European Journal of Electrical Engineering and Computer Science. 4, 4 (Jul. 2020). DOI:https://doi.org/10.24018/ejece.2020.4.4.225.