Analysis of techniques and approaches to palm print: Review

Document Type : Review articles

Authors

Department of computer science, College of science, University of Diyala, Baqubah, Iraq

Abstract

For over 15 years, palmprint identification technology has been developed and tested on a range of image resolutions (high and low). This study demonstrates the numerous varieties of palmprints and the difficulties associated with the palmprint recognition method. Furthermore, we go over the step-by-step process of developing a palmprint biometrics system, starting with image acquisition, preprocessing, feature extraction, and matching, as well as a summary of palmprint databases and their characterizations, as well as some palmprint recognition techniques and research works related to palmprint biometrics purposes. This paper focuses on comparing the types of systems in terms of deep learning, machine learning, and systems that require learning.

Keywords

[1] Y. Alberni, L. Boubchir and B. Daachi, Multispectral palmprint recognition: A state-of-the-art review, 40th Int. Conf. Telecommun. Signal Process. (TSP), IEEE, 2017, pp. 793–797.
[2] M.A. Al-Garadi, A. Mohamed, A.K. Al-Ali, X. Du, I. Ali and M. Guizani, A survey of machine and deep learning methods for internet of things (IoT) security, IEEE Commun. Surv. Tutorials 22 (2020), no. 3, 1646–1685.
[3] M.M.H. Ali, V.H. Mahale, P. Yannawar and A.T. Gaikwad, Study of edge detection methods based on palmprint lines, Int. Conf. Electric. Electron. Optim. Tech.(ICEEOT), IEEE, 2016, pp. 1344–1350.
[4] M.M.H. Ali, V.H. Mahale, P.L. Yannawar and A.T. Gaikwad, A review: Palmprint recognition process and techniques, Int. J. Appl. Eng. Res. 13 (2018), no. 10, 7499–7507.
[5] B. Attallah, Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction, J. Electro. Imag. 26 (2021), no. 6.
[6] V. Basa, Supervisor-supervisee relationship and alliance, Eur. J. Couns. Theory, Res. Pract. 1 (2017), no. 10, 1–5.
[7] K. Bensid, D. Samai, F.Z. Laallam and A. Meraoumia, Deep learning feature extraction for multispectral palmprint identification, J. Electron. Imag. 27 (2018), no. 3, p. 033018.
[8] L. Fei, G. Lu, W. Jia, S. Teng and D. Zhang, Feature extraction methods for palmprint recognition: A survey and evaluation, IEEE Trans. Syst. Man, Cybern. Syst. 49 (2019), no. 2, 346–363.
[9] L. Fei, Y. Xu and D. Zhang, Half-orientation extraction of palmprint features, Pattern Recog. Lett. 69 (2016), 35–41.
[10] L. Fei, B. Zhang, W. Jia, J. Wen and D. Zhang, Feature extraction for 3d palmprint recognition: A survey, IEEE Trans. Instrum. Meas. 69 (2020), no. 3, 645–656.
[11] L. Fei, B. Zhang, Y. Xu, Z. Guo, J. Wen and W. Jia, Learning discriminant direction binary palmprint descriptor, IEEE Trans. Image Process. 28 (2019), no. 8, 3808–3820.
[12] J.I. Funada, N. Ohta, M. Mizoguchi, T. Temma, K. Nakanishi, A. Murai, T. Sugiuchi, T. Wakabayashi and Y. Yamada, Feature extraction method for palmprint considering elimination of creases, Proc. Fourteenth Int. Conf. Pattern Recogn. (Cat. No. 98EX170), IEEE, 2 (1998), 1849–1854.
[13] C.C. Han, H.L. Cheng, C.L. Lin and K.C. Fan, Personal authentication using palm-print features, Pattern recognit. 32 (2003), no. 2, 371–381.
[14] A. Iula, S. Member, D. Nardiello and S. Member, 3-D ultrasound palmprint recognition system based on principal lines extracted at several under skin depths, IEEE Trans. Instrum. Meas. 68 (2019), no. 12, 4653–4662.
[15] M. Izadpanahkakhk, S.M. Razavi, M. Taghipour-Gorjikolaie, S.H. Zahiri and A. Uncini, Deep region of interest and feature extraction models for palmprint verification using convolutional neural networks transfer learning, Appl. Sci. 8 (2018), no. 7, 1–20.
[16] A.K. Jain and J. Feng, Latent palmprint matching, IEEE Trans. Pattern Anal. Machine Intell. 31 (2008), no. 6, 1032–1047.
[17] A.K. Jain, S. Pankanti, S. Prabhakar, H. Lin and A. Ross, Biometrics: A grand challenge, Proc. Int. Conf. Pattern Recog. 2 (2004), 935–942.
[18] W. Jia, B. Zhang, J. Lu, Y. Zhu, Y. Zhao, W. Zuo and H. Ling, Palmprint recognition based on complete direction representation, IEEE Trans. Image Process. 26 (2017), no. 9, 4483–4498.
[19] P. Kamboj and S. Bala, Review paper on enhancing palm print recognition system, Int. J. Sci. Res. Dev. 3 (2015), no. 01, 705–709.
[20] M. Kastek, R. Dulski, P. Trzaskawka, T. Sosnowski and H. Madura, Concept of infrared sensor module for sniper detection system, 35th Int. Conf. Infrared, Millimeter, and Terahertz Waves, IEEE, 2010, pp. 1–2.
[21] S. Kaushik and R. Singh, A new hybrid approach for palmprint recognition in PCA based palmprint recognition system, 5th Int. Conf. Reliab. Infocom Technol. Optim. (Trends and Future Directions)(ICRITO), IEEE, 2016, pp. 239–244.
[22] A. Kong, D. Zhang and M. Kamel, A survey of palmprint recognition, Pattern Recognit. 42 (2009), no. 7, 1408–1418.
[23] B.G. Kuhnh¨auser, S. Bellot, T.L. Couvreur, J. Dransfield, A. Henderson, R. Schley, G. Chomicki, W.L. Eiserhardt, S.J. Hiscock and W.J. Baker, A robust phylogenomic framework for the calamoid palms, Mol. Phylogenet. Evol. 157 (2021), 107067.
[24] S. Lin, T. Xu and X. Yin, Region of interest extraction for palmprint and palm vein recognition, 9th Int Cong Image Signal Process BioMed Engin Inf (CISP-BMEI), IEEE, 2016, pp. 538–542.
[25] F. Liu, L. Zhou, Z.M. Lu and T. Nie, Palmprint feature extraction based on curvelet transform, J. Info. Hiding Multimed. Signal Process. 6 (2015), no. 1, 131–139.
[26] P. Manegopale, A survey on palmprint recognition, Int. J. Innov. Res. Sci. Eng. Tech. 3 (2014), no. 2, 9085–9094.
[27] M.S. Manoj and S. Arulselvi, Palmprint identification and classification using KNN algorithm, Mater. Today Proc. 2021.
[28] P. Matteo Barone, Forensic geo-archaeology in Italy: Materials for a standardisation, Int. J. Archaeol. 3 (2015), no. 1, p. 45.
[29] A. Mishra, Multimodal biometrics it is: need for future systems, Int. J. Comput. Appl. 3 (2010), no. 4, 28–33.
[30] R. Mokni, R. Zouari and M. Kherallah, Pre-processing and extraction of the ROIs steps for palmprints recognition system, 15th Int. Conf. Intell. Syst. Design Appl. (ISDA), IEEE, 2015, pp. 380–385.
[31] A. Morales, M.A. Ferrer and A. Kumar, Towards contactless palmprint authentication, IET Comput. Vis. 5 (2011), no. 6, 407–416.
[32] J.P. Patil and C. Nayak, A survey of multispectral palmprint identification techniques, Int. J. Sci. Eng. Tech. 3 (2014), no. 8, 1051–1053.
[33] E. Piciucco, E. Maiorana and P. Campisi, Palm vein recognition using a high dynamic range approach, IET Biometrics 7 (2018), no. 5, 439–446.
[34] P. Poonia, P.K. Ajmera and V. Shende, Palmprint recognition using robust template matching, Procedia Comput. Sci. 167 (2020), no. 2019, 727–736.
[35] R. Raghavendra and C. Busch, Texture-based features for robust palmprint recognition: A comparative study, Eurasip J. Inf. Secur. 2015 (2015), no. 1, 1–9.
[36] I. Rida, R. Herault, G.L. Marcialis and G. Gasso, Palmprint recognition with an efficient data-driven ensemble classifier, Pattern Recognit. Lett. 126 (2019), 21–30.
[37] M.A.W. Shalaby, Fingerprint recognition: A histogram analysis based fuzzy c-means multilevel structural approach, PhD Diss. Concordia University, 2012.
[38] H. Shao, D. Zhong and X. Du, Cross-domain palmprint recognition based on transfer convolutional autoencoder, IEEE Int. Conf. Image Process. (ICIP), IEEE, 2019, pp. 1153–1157.
[39] D. Smorawa and M. Kubanek, Biometric systems based on palm vein patterns, J. Telecommun. Inf. Technol. 2015 (2015), no. 2, 18–22.
[40] S.C. Soh, M.Z. Ibrahim and M.B. Yakno, A review: Personal identification based on palm vein infrared pattern, J. Telecommun. Electron. Comput. Eng. 10 (2018), no. 1–4, 175–180.
[41] K. Suzuki, Overview of deep learning in medical imaging, Radiol. Phys. Technol. 10 (2017), no. 3, 257–273.
[42] D. Tamrakar and P. Khanna, Occlusion invariant palmprint recognition with ULBP histograms, Procedia Comput. Sci. 54 (2015), 491–500.
[43] A. Theofilatos, C. Chen and C. Antoniou, Comparing machine learning and deep learning methods for real-time crash prediction, Transp. Res. Rec. 2673 (2019), no. 8, 169–178.
[44] A.S. Ungureanu, S. Salahuddin and P. Corcoran, Toward unconstrained palmprint recognition on consumer devices: A literature review, IEEE Access 8 (2020), 86130–86148.
[45] A. Verma and P. Tiwari, Personal palm print identification using KNN classifier, Int. J. Modern Engin. Manag. Res. 7 (2019), no. 4, 62–67.
[46] F. Wang, L. Leng, A.B.J. Teoh and J. Chu, Palmprint false acceptance attack with a generative adversarial network (Gan), Appl. Sci. 10 (2020), no. 23, 1–16.
[47] W. Wu, S.J. Elliott, S. Lin, S. Sun and Y. Tang, Review of palm vein recognition, IET Biometrics 9 (2020), no. 1, 1–10.
[48] L. Wu, Y. Xu, Z. Cui, Y. Zuo, S. Zhao and L. Fei, Triple-type feature extraction for palmprint recognition, Sensors 21 (2021), no. 14, 1–15.
[49] Y. Xin, L. Kong, Z. Liu, Y. Chen, Y. Li, H. Zhu, M. Gao, H. Hou and C. Wang, Machine learning and deep learning methods for cybersecurity, IEEE Access 6 (2018), 35365–35381.
[50] X. Yang, J. Feng and J. Zhou, Palmprint indexing based on ridge features, Int. Joint Conf. Biometrics (IJCB), IEEE, 2011, pp. 1–8.
[51] A. Younesi and M.C. Amirani, Gabor filter and texture based features for palmprint recognition, Procedia Comput. Sci. 108 (2017), 2488–2495.
[52] L. Zhang, Z. Cheng, Y. Shen and D. Wang, Palmprint and palm-vein recognition based on DCNN and a new large-scale contactless palmvein dataset, Symmetry 10 (2018), no. 4, p. 78.
[53] Q. Zheng, A. Kumar and G. Pan, Suspecting less and doing better: New insights on palmprint identification for faster and more accurate matching, IEEE Trans. Inf. Forensics Secur. 11 (2016), no. 3, 633–641.
[54] D. Zhong, X. Du and K. Zhong, Decade progress of palmprint recognition: A brief survey, Neurocomput. 328 (2019), 16–28.
[55] Z. Zhu, X. Chen, Y. Tu and X. Zhang, Palmprint image acquisition and analysis system based on IoT technology, OALib 7 (2020), no. 11, 1–8.
[58] http://www4.comp.polyu.edu.hk/∼biometrics/Multisp ectralPalmprint/MSP.html.
[61] http://www4.comp.polyu.edu.hk/∼csajaykr/IITD/Database Palm.html.
Volume 13, Issue 2
July 2022
Pages 887-898
  • Receive Date: 12 January 2022
  • Revise Date: 23 March 2022
  • Accept Date: 09 April 2022