Fingerprint liveness detection based on contourlet, various entropy algorithms and multiobjective genetic algorithm-based ensemble classifier

References

• Ratha NK, Connell JH, Bolle RM. Enhancing security and privacy in biometrics-based authentication systems. IBM Syst J. 2001;40(3):614–634. doi: 10.1147/sj.403.0614
   Google Scholar
• Patil S, Tajane K, Sirdeshpande J. Enhancing security and privacy in biometrics based authentication system using multiple secret sharing. In 2015 International Conference on Computing Communication Control and Automation, Pune, India. IEEE; 2015.
   Google Scholar
• Uludag U, Jain AK. Attacks on biometric systems: a case study in fingerprints. In: Edward J. Delp, Ping W Wong, editors. Security, steganography, and watermarking of multimedia contents VI. Bellingham, Washington USA: SPIE; 2004:622–633.
   Google Scholar
• Baldisserra D, Franco A, Maio D, et al. Fake fingerprint detection by odor analysis. In Advances in Biometrics: International Conference, ICB 2006. Hong Kong, China, January 5–7, 2006. Proceedings; 2005, Springer.
   Google Scholar
• Kang H, Lee B, Kim H, et al. A study on performance evaluation of the liveness detection for various fingerprint sensor modules. In Knowledge-Based Intelligent Information and Engineering Systems: 7th International Conference, KES 2003, Oxford, UK, September 2003. Proceedings, Part II 7, Oxford, UK. Springer; 2003.
   Google Scholar
• Khammari M. Weber pattern and binarized statistical image features encoded CNN for fingerprint liveness detection. In 2019 6th International Conference on Image and Signal Processing and their Applications (ISPA), Mostaganem, Algeria. IEEE; 2019.
   Google Scholar
• Maltoni D, Maio D, Jain AK, et al. Handbook of fingerprint recognition. Vol. 2. London: Springer; 2009.
   Google Scholar
• Zhang Y, Gao C, Pan S, et al. A score-level fusion of fingerprint matching with fingerprint liveness detection. IEEE Access. 2020;8:183391–183400. doi: 10.1109/ACCESS.2020.3027846
   Web of Science ®Google Scholar
• Khaleghi A, Mohammadi MR, Shahi K, et al. Computational neuroscience approach to psychiatry: a review on theory-driven approaches. Clin Psychopharmacol Neurosci. 2022;20(1):26. doi: 10.9758/cpn.2022.20.1.26
   PubMed Web of Science ®Google Scholar
• Marcel S, Nixon MS, Li SZ. Handbook of biometric anti-spoofing. Vol. 1. London: Springer; 2014.
   Google Scholar
• Nixon K, Rowe R. Spoof detection using multispectral fingerprint imaging without enrollment. In Proceedings of Biometrics Symposium (BSYM2005); Arlington, VA; 2005.
   Google Scholar
• Antonelli A, Cappelli R, Maio D, et al. Fake finger detection by skin distortion analysis. IEEE Trans Inf Forensics Secur. 2006;1(3):360–373. doi: 10.1109/TIFS.2006.879289
   Web of Science ®Google Scholar
• Marasco E, Sansone C. An anti-spoofing technique using multiple textural features in fingerprint scanners. In 2010 IEEE workshop on biometric measurements and systems for security and medical applications, Taranto, Italy. IEEE; 2010.
   Google Scholar
• Marasco E, Sansone C. On the robustness of fingerprint liveness detection algorithms against new materials used for spoofing. Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, Portugal; 2011:553–558. d i: 0.5220/0003270505530558
   Google Scholar
• Tan B, Lewicke A, Schuckers S. Novel methods for fingerprint image analysis detect fake fingers. SPIE Newsroom. 2008;30:1–3. doi: 10.1117/2.1200705.1171
   Google Scholar
• Warwante B, Maske M. Wavelet based fingerprint liveness detection. Int J Eng Res Appl. 2012;2(2):1643–1645.
   Google Scholar
• Matsumoto T, Matsumoto H, Yamada K, et al. Impact of artificial “gummy” fingers on fingerprint systems. In: Rudolf L, van Renesse. Optical security and counterfeit deterrence techniques IV. Bellingham, Washington USA: SPIE; 2002:275–289.
   Google Scholar
• Nikam SB, Agarwal S. Texture and wavelet-based spoof fingerprint detection for fingerprint biometric systems. In 2008 first international conference on emerging trends in engineering and technology, Nagpur, India. IEEE; 2008.
   Google Scholar
• Reddy PV, Kumar A, Rahman SMK, et al. A new antispoofing approach for biometric devices. IEEE transactions on biomedical circuits and systems. IEEE Trans Biomed Circuits Syst. 2008;2(4):328–337. doi: 10.1109/TBCAS.2008.2003432
   PubMed Web of Science ®Google Scholar
• Adam EEB. Evaluation of fingerprint liveness detection by machine learning approach-a systematic view. J Ismac. 2021;3(1):16–30. doi: 10.36548/jismac.2021.1.002
   Google Scholar
• Marcialis GL, Lewicke A, Tan B, et al. First international fingerprint liveness detection competition—LivDet 2009. In: Image Analysis and Processing–ICIAP 2009: 15th International Conference Vietri sul Mare., Italy, September 8–11, 2009 Proceedings 15. Springer; 2009.
   Google Scholar
• Ghiani L, Yambay DA, Mura V, et al. Review of the fingerprint liveness detection (LivDet) competition series: 2009–2015. Image Vision Comput. 2017;58:110–128. doi: 10.1016/j.imavis.2016.07.002
   Web of Science ®Google Scholar
• Micheletto M, Orrù G, Casula R, et al. Review of the fingerprint liveness detection (LivDet) competition series: from 2009 to 2021. Handb Biometric Anti-Spoofing: Presentation Attack Detection Vulnerability Assessment. 2023;1:57–76.
   Google Scholar
• Kulkarni SS, Patil HY. Survey on fingerprint spoofing, detection techniques and databases. Int J Comput Appl. 2015;975:8887.
   Google Scholar
• Galbally J, Fierrez J, Alonso-Fernandez F, et al. Evaluation of direct attacks to fingerprint verification systems. Telecommun Syst. 2011;47(3–4):243–254. doi: 10.1007/s11235-010-9316-0
   Web of Science ®Google Scholar
• Buades A, Coll B, Morel J-M. A review of image denoising algorithms, with a new one. Multiscale Model & Simul. 2005;4(2):490–530. doi: 10.1137/040616024
   Web of Science ®Google Scholar
• Rasti B, Scheunders P, Ghamisi P, et al. Noise reduction in hyperspectral imagery: overview and application. Remote Sens. 2018;10(3):482. doi: 10.3390/rs10030482
   Google Scholar
• You Y-L, Kaveh M. Fourth-order partial differential equations for noise removal. IEEE Trans Image Process. 2000;9(10):1723–1730. doi: 10.1109/83.869184
   PubMed Web of Science ®Google Scholar
• Do MN, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process. 2005;14(12):2091–2106. doi: 10.1109/TIP.2005.859376
   PubMed Web of Science ®Google Scholar
• Da Cunha AL, Zhou J, Do MN. The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans Image Process. 2006;15(10):3089–3101. doi: 10.1109/TIP.2006.877507
   PubMed Web of Science ®Google Scholar
• Huang Z, Li X, Wang L, et al. Spatially adaptive multiscale image enhancement based on nonsubsampled contourlet transform. Infrared Phys & Technol. 2022;121:104014. doi: 10.1016/j.infrared.2021.104014
   Web of Science ®Google Scholar
• Li X, Zhou F, Tan H, et al. Multi-focus image fusion based on nonsubsampled contourlet transform and residual removal. Signal Process. 2021;184:108062. doi: 10.1016/j.sigpro.2021.108062
   Web of Science ®Google Scholar
• Manikandan T, Karthikeyan S, Jai Jaganath Babu J, et al. Adaptive fuzzy logic despeckling in non-subsampled contourlet transformed ultrasound pictures. Intell Automation & Soft Comput. 2023;35(3):2755–2771. doi: 10.32604/iasc.2023.030497
   Web of Science ®Google Scholar
• Singh P, Diwakar M, Singh S, et al. A homomorphic non-subsampled contourlet transform based ultrasound image despeckling by novel thresholding function and self-organizing map. Biocybernetics Biomed Eng. 2022;42(2):512–528. doi: 10.1016/j.bbe.2022.03.003
   Web of Science ®Google Scholar
• Wu Y, Zhou Y, Saveriades G, et al. Local Shannon entropy measure with statistical tests for image randomness. Inf Sci. 2013;222:323–342. doi: 10.1016/j.ins.2012.07.049
   Web of Science ®Google Scholar
• Mohammadi MR, Khaleghi A, Nasrabadi AM, et al. EEG classification of ADHD and normal children using nonlinear features and neural network. Biomed Eng Lett. 2016;6(2):66–73. doi: 10.1007/s13534-016-0218-2
   Web of Science ®Google Scholar
• Rizal A, Hadiyoso S. Sample entropy on multidistance signal level difference for epileptic EEG classification. Sci World J. 2018;2018:1–6. doi: 10.1155/2018/8463256
   Google Scholar
• Li J, Yan J, Liu X, et al. Using permutation entropy to measure the changes in EEG signals during absence seizures. Entropy. 2014;16(6):3049–3061. doi: 10.3390/e16063049
   Web of Science ®Google Scholar
• Miskovic V, MacDonald KJ, Rhodes LJ, et al. Changes in EEG multiscale entropy and power‐law frequency scaling during the human sleep cycle. Human Brain Mapp. 2019;40(2):538–551. doi: 10.1002/hbm.24393
   PubMed Web of Science ®Google Scholar
• Das AB, Bhuiyan MIH. Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomed Signal Process Control. 2016;29:11–21. doi: 10.1016/j.bspc.2016.05.004
   Web of Science ®Google Scholar
• Simons S, Espino P, Abásolo D. Fuzzy entropy analysis of the electroencephalogram in patients with Alzheimer’s disease: is the method superior to sample entropy? Entropy. 2018;20(1):21. doi: 10.3390/e20010021
   PubMed Web of Science ®Google Scholar
• Hosseini SA, Naghibi-Sistani MB. Emotion recognition method using entropy analysis of EEG signals. Int J Image Graphics Signal Process. 2011;3(5):30. doi: 10.5815/ijigsp.2011.05.05
   Google Scholar
• Gupta V, Priya T, Yadav AK, et al. Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform. Pattern Recognit Lett. 2017;94:180–188. doi: 10.1016/j.patrec.2017.03.017
   Web of Science ®Google Scholar
• Khaleghi A, Sheikhani A, Mohammadi MR, et al. EEG classification of adolescents with type I and type II of bipolar disorder. Australasian physical & engineering sciences in medicine, 2015;38(4):551–559. doi: 10.1007/s13246-015-0375-0
   Google Scholar
• Cortes C, Vapnik V. Support-vector networks. Vol. 20. Boston: Machine learning; 1995. p. 273–297.
   Google Scholar
• Patle A, Chouhan DS. SVM kernel functions for classification. In 2013 International Conference on Advances in Technology and Engineering (ICATE), Mumbai, India. IEEE; 2013.
   Google Scholar
• Khaleghi A, Mohammadi MR, Moeini M, et al. Abnormalities of alpha activity in frontocentral region of the brain as a biomarker to diagnose adolescents with bipolar disorder. Clinical EEG and neuroscience. Clin EEG Neurosci. 2019;50(5):311–318. doi: 10.1177/1550059418824824
   PubMed Web of Science ®Google Scholar
• Huang K, Li S, Kang X, et al. Spectral–spatial hyperspectral image classification based on KNN. Sens Imag. 2016;17(1):1–13. doi: 10.1007/s11220-015-0126-z
   Google Scholar
• Choubey DK, Kumar M, Shukla V, et al. Comparative analysis of classification methods with PCA and LDA for diabetes. Curr Diabetes Rev. 2020;16(8):833–850. doi: 10.2174/18756417MTAz5ODYi2
   PubMed Web of Science ®Google Scholar
• Ye F, Shi Z, Shi Z. A comparative study of PCA, LDA and kernel LDA for image classification. In 2009 International Symposium on Ubiquitous Virtual Reality, Guangju, Korea (South). IEEE; 2009.
   Google Scholar
• Galbally J, Alonso-Fernandez F, Fierrez J, et al. A high performance fingerprint liveness detection method based on quality related features. future generation computer systems. Future Gener Comput Syst. 2012;28(1):311–321. doi: 10.1016/j.future.2010.11.024
   Web of Science ®Google Scholar
• Marasco E, Sansone C. Combining perspiration-and morphology-based static features for fingerprint liveness detection. Pattern Recognit Lett. 2012;33(9):1148–1156. doi: 10.1016/j.patrec.2012.01.009
   Web of Science ®Google Scholar
• Galbally J, Marcel S, Fierrez J. Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition. IEEE Trans Image Process. 2013;23(2):710–724. doi: 10.1109/TIP.2013.2292332
   Web of Science ®Google Scholar
• Gragnaniello D, Poggi G, Sansone C, et al. Wavelet‐Markov local descriptor for detecting fake fingerprints. Electron Lett. 2014;50(6):439–441. doi: 10.1049/el.2013.4044
   Web of Science ®Google Scholar
• Nogueira RF, de Alencar Lotufo R, Machado RC. Evaluating software-based fingerprint liveness detection using convolutional networks and local binary patterns. In 2014 IEEE workshop on biometric measurements and systems for security and medical applications (BIOMS) Proceedings, Rome, Italy. IEEE; 2014.
   Google Scholar
• Jiang Y, Liu X. Spoof fingerprint detection based on co-occurrence matrix. Int J Signal Process, Image Process And Pattern Recognit. 2015;8(8):373–384. doi: 10.14257/ijsip.2015.8.8.38
   Google Scholar