Abstract
Palm print scanning is a widespread method for biometric identity detection which has some advantages over other methods including its simplicity and relatively lower cost. In this study, a novel methods for biometric verification and identification by contactless palm scanning technique is proposed. In the study, Ripplet-I Transform (R-IT) which is a generalized form of Curvelet Transform (CuT), have been used in addition to multi-resolution transforms which were previously used in the literature as palm print verification and identification methods such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Contourlet Transform (CoT). In addition, Principal Component Analysis (PCA) and Local Binary Pattern (LBP) have been utilized to increase the algorithm diversity. In order to investigate the effect of classification methods on the study results and the processing times, Artificial Neural Network (ANN), Euclidean Distance (ED) and Support Vector Machine (SVM) have been used separately for matching in the verification part of study. The performance of Convolutional Neural Network (CNN) as a classifier has also been examined. Verification and identification algorithms proposed in the study have been tested using palm print images of Hong Kong Polytechnic University Contact-free 3D/2D Hand Images Database (Version 1.0). The studies, that were carried out under two main sections yielded interesting results. At the end of the study, AUC (Area Under the ROC Curve) values ranging from 0.550 (Equal Error Rate (EER)= 0.4594) to 0.9875 (EER= 0.0336) were obtained for palm print verification. The highest AUC value without using LBP was obtained as 0.9563 (EER= 0.1096) using R-IT/CuT+DCT+CNN. Study results were showed that CNN is more successful than other classifiers without using LBP. It also has pointed out that the R-IT/CuT provides better results than the DWT and CoT. Using LBP in algorithms has increased success for ED, SVM and ANN. However, it has reduced overall for CNN. The highest AUC value (0.9875 and EER= 0.0336) was provided by the LBP+DWT+ED algorithm for palm print verification. The highest Identification Rate (IR) was achieved by using the LBP+CoT+ED algorithm with 84.444% for for palm print identification.
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References
Ahmad MI, Ilyas MZ, Ngadiran R, Isa MN, Yaakob SN (2014) Palmprint recognition using local and global features. In: International conference on systems, signals and image processing, pp 79–82
Bamberger RH, Smith MJ (1992) A filter bank for the directional decomposition of images: theory and design. IEEE Trans Signal Process 40(4):882–93
Candes E, Demanet L, Donoho D, Ying L (2006) Fast discrete curvelet transforms. Multiscale Modeling and Simulation 5(3):861–99
Candes EJ, Donoho DL (2000) Curvelets: a surprisingly effective nonadaptive representation for objects with edges. Available from: http://www.dtic.mil/dtic/tr/fulltext/u2/p011978.pdf
Ceylan M, Yaşar H (2016) A novel approach for automatic blood vessel extraction in retinal images: complex ripplet-I transform and complex valued artificial neural network. Turk J Electr Eng Comput Sci 24(4):3212–27
Chen GY, Kégl B (2010) Invariant pattern recognition using contourlets and AdaBoost. Pattern Recogn 43(3):579–83
Chen XH, Li CZ (2009) Cross–band fusion by energy weight as solution to illumination and arch restrictions in palm–print recognition. Int J Imaging Syst Technol 19(4):350–5
Choge HK, Oyama T, Karungaru S, Tsuge S, Fukumi M (2009) Palmprint recognition based on local DCT feature extraction. In: International conference on neural information processing, pp 639–648
Cummins H, Midlo C (1961) Finger prints palms and soles: an introduction to dermatoglyphics. Dover Publications, New York
Dale MP, Joshi MA, Gilda N (2009) Texture based palmprint identification using DCT features. In: International conference on advances in pattern recognition, pp 221–224
Daubechies I (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Information Theor 36(5):961–1005
Dewan S (2003) Elementary, watson: scan a palm, find a clue. Available from: https://www.nytimes.com/2003/11/21/nyregion/elementary-watson-scan-a-palm-find-a-clue.html
Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14(12):2091–106
Galton F (1965) Fingerprints. Da Capo Press, Boston-Massachusetts
Goh MK, Connie T, Teoh AB, Ngo DC (2006) A fast palm print verification system. In: International conference on computer graphics, imaging and visualisation, pp 168–172
Imtiaz H, Aich S, Fattah SA (2014) Palm-print recognition based on DCT domain statistical features extracted from enhanced image. In: International conference on electrical engineering and information and communication technology, pp 1–4
Imtiaz H, Fattah SA (2010) A DCT-based feature extraction algorithm for palm-print recognition. In: International conference on communication control and computing technologies, pp 657–660
Imtiaz H, Fattah SA (2013) A wavelet-based dominant feature extraction algorithm for palm-print recognition. Digital Signal Processing 23(1):244–58
Isnanto RR, Septiana R, Zahra AA, Iskandar IK, Wicaksono G (2017) Comparison analysis between implementation of principal components analysis and haar wavelet as feature extractors in palmprint recognition system. In: Second international conference on informatics and computing, pp 1–6
Jaswal G, Nath R, Kaul A (2015) Multiple resolution based palm print recognition using 2d-DWT and Kernel PCA. In: International conference on signal processing and communication, pp 210–215
Kanchana S, Balakrishnan G (2015) A novel Gaussian measure curvelet based feature segmentation and extraction for palmprint images. Indian J Sci Technol 8(15):1–7
Kanhangad V, Kumar A, Zhang D (2011) A unified framework for contactless hand verification. IEEE Trans Inform Forensics Secur 6(3):1014–27
Kisku DR, Rattani A, Gupta P, Hwang CJ, Sing JK (2011) Palmprint identification using FRIT. In: Mobile multimedia/image processing, security, and applications , vol 8063, p 80630T
Leng L, Li M, Kim C, Bi X (2017) Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimed Tools Appl 76 (1):333–354
Leng L, Teoh ABJ, Li M, Khan MK (2015) Orientation range of transposition for vertical correlation suppression of 2DPalmphasor code. Multimed Tools Appl 74 (24):11683–11701
Leng L, Zhang J (2013) Palmhash code vs. palmphasor code. Neurocomputing 108:1–12
Leng L, Zhang J, Chen G, Khan MK, Alghathbar K (2011) Two-directional two-dimensional random projection and its variations for face and palmprint recognition. In: International conference on computational science and its applications, pp 458–470
Leng L, Zhang J, Xu J, Khan MK, Alghathbar K (2010) Dynamic weighted discrimination power analysis in DCT domain for face and palmprint recognition. In: 2010 international conference on information and communication technology convergence, pp 467–471
Leng L, Zhang JS, Khan MK, Bi X, Ji M (2010) Cancelable palmcode generated from randomized gabor filters for palmprint protection. In: International conference of image and vision computing, pp 1–6
Lu J, Zhao Y, Xue Y, Hu J (2008) Palmprint recognition via locality preserving projections and extreme learning machine neural network. In: International conference on signal processing, pp 2096–2099
Masood H, Mumtaz M, Butt MA, Mansoor AB, Khan SA (2008) Wavelet based palmprint authentication system. In: International symposium on biometrics and security technologies, pp 1–7
Murukesh C, Elango GA (2018) Multi-algorithmic palmprint authentication system based on score level fusion. International Journal on Smart Sensing and Intelligent Systems 1(18):1–11
NSTC Subcommittee on Biometrics (2009) Palm print recognition. Available from: www.fbi.gov/file-repository/about-us-cjis-fingerprints_biometrics-biometric-center-of-excellences-palm-print-recognition.pdf
Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–9
Pan X, Ruan Q, Wang Y (2008) Palmprint recognition using contourlets-based local fractal dimensions. In: International conference on signal processing, pp 2108–2111
Patil JP, Nayak C, Jain M (2015) Palmprint recognition using DWT, DCT and PCA techniques. In: International conference on computational intelligence and computing research, pp 1–5
Patil P, Kumar KS, Gaud N, Semwal VB (2019) Clinical human gait classification: extreme learning machine approach. in international conference on advances in science. Engineering and Robotics Technology, pp 1–6
Prasad SM, Govindan VK, Sathidevi PS (2011) Palmprint authentication using fusion of wavelet and contourlet features. Security and Communication Networks 4(5):577–90
Ramteke RJ, Alsubari A (2016) Extraction of palmprint texture features using combined DWT-DCT and local binary pattern. In: International conference on next generation computing technologies, pp 748–753
Rios-Sánchez B, Viana-Matesanz M, Sánchez-Ávila C (2017) Curvelets for contact-less hand biometrics under varied environmental conditions. In: International carnahan conference on security technology, pp 1–6
Sanyal N, Chatterjee A, Munshi S (2015) A novel palmprint authetication system by XWT based feature extraction and BFOA based feature selection and optimization. In: International conference on recent trends in information systems, pp 455–460
Sanyal N, Chatterjee A, Munshi S (2017) BFOA with varying population based feature selection and optimization in palm print authentication—a comparative study. In: IEEE calcutta conference , pp 236–240
Saranraj S, Padmapriya V, Sudharsan S, Piruthiha D, Venkateswaran N (2016) Palm print biometric recognition based on scattering wavelet transform. In: International conference on wireless communications, signal processing and networking, pp 490–495
Semwal VB, Gaud N, Nandi GC (2019) Human gait state prediction using cellular automata and classification using ELM. In: Machine intelligence and signal analysis, pp 135–145
Semwal VB, Raj M, Nandi GC (2014) Multilayer perceptron based biometric GAIT identification. Robot Auton Syst 21
Semwal VB, Singha J, Sharma PK, Chauhan A, Behera B (2017) An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification. Multimed Tools Appl 76(22):24457–24475
Shashikala KP, Raja KB (2012) Palmprint identification using transform domain and spatial domain techniques, pp 105–109
Sirovich L, Kirby M (1987) Low-dimensional procedure for the characterization of human faces. J Opt Soc Am A 4(3):519–24
Tamrakar D, Khanna P (2010) Analysis of palmprint verification using wavelet filter and competitive code. In: International conference on computational intelligence and communication networks, pp 20–25
The Hong Kong Polytechnic University Contact-free 3D/2D Hand Images Database Version 1.0 (2011) Available from: http://www4.comp.polyu.edu.hk/~csajaykr/myhome/database_request/3dhand/Hand3D.htm
Thepade SD, Gudadhe SS (2013) Palm print identification using fractional coefficient of transformed edge palm images with Cosine, Haar and Kekre transform. In: IEEE conference on information and communication technologies, pp 1232–1236
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3 (1):71–86
Vapnik V, Chervonenkis A (1964) A note on one class of perceptrons. Autom Remote Control 25
Varshney V, Gupta R, Singh P (2014) Hybrid DWT-DCT based method for palm-print recognition. In: International symposium on signal processing and information technology, pp 000007–000012
Wang YX, Sun GH (2012) Palmprint recognition using Palm-line direction field texture feature. In: International conference on machine learning and cybernetics, vol 3, pp 1130–1134
Wu XQ, Wang KQ, Zhang D (2002) Wavelet based palm print recognition. In: Proceedings international conference on machine learning and cybernetics, vol 3, pp 1253–1257
Xinchun W, Kaihua Y, Yuming L, Qing Y (2011) Palmprint recognition based on curvelet transform decision fusion. Procedia Engineering 23:303–9
Xu J, Yang L, Wu D (2010) Ripplet: a new transform for image processing. J Vis Commun Image Represent 21(7):627–39
Yaşar H, Ceylan M (2016) New approaches based on real and complex forms of ripplet-I transform for image analysis. In: Signal processing and communication application conference, pp 745–748
Yu PF, Xu D (2008) Palmprint recognition based on modified DCT features and RBF neural network. In: International conference on machine learning and cybernetics, vol 5, pp 2982–2986
Zhang S, Wang S, Li X (2008) Palmprint linear feature extraction and identification based on ridgelet transforms and rough sets. In: International conference on intelligent computing, pp 1101–1108
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Dr. Hardalac declares that he has no conflict of interest. Mr. Yasar declares that he has no conflict of interest. Mr. Akyel declares that he has no conflict of interest. Dr. Kutbay declares that he has no conflict of interest.
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Hardalac, F., Yaşar, H., Akyel, A. et al. A novel comparative study using multi-resolution transforms and convolutional neural network (CNN) for contactless palm print verification and identification. Multimed Tools Appl 79, 22929–22963 (2020). https://doi.org/10.1007/s11042-020-09005-2
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DOI: https://doi.org/10.1007/s11042-020-09005-2