[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 palmvein 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.
[56] http://biometrics.idealtest.org/dbDetailForUser.do?id=6.
[57] http://biometrics.idealtest.org/dbDetailForUser.do?id=5.
[58] http://www4.comp.polyu.edu.hk/∼biometrics/Multisp ectralPalmprint/MSP.html.
[59] http://www4.comp.polyu.edu.hk/∼biometrics/.
[60] www.kvkale.in/.
[61] http://www4.comp.polyu.edu.hk/∼csajaykr/IITD/Database Palm.html.
[62] https://www.fbi.gov/services/cjis/cjis-link/national-palm-print-system.
[63] https://www.coep.org.in/resources/coeppalmprintdatabase.
[64] http://ivg.au.tsinghua.edu.cn/dataset/THUPALMLAB.php.