Abstract
This paper present a novel system for person authentication based on score level fusion of Minor and Major dorsal finger knuckle patterns. In the proposed method, the adaptive single scale retinex method is used to extract the reflectance and the illumination of Major and Minor traits respectively, also the binarized statistical image features method is used to extract normalized histogram features. Furthermore, the Cosine Mahalanobis distance is used in the matching stage. Moreover, a multi-biometric system based score level fusion has been proposed. In attempt to enhance the performance recognition, the symmetric sum-based rules based on triangular norms are applied. The system is evaluated on the publically Minor/Major knuckle database. Experiments conducted on this database achieved good results. Besides, the proposed system outperforms the previous methods given in the state of the art.
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The data is publicly available (publicly available finger knuckle biometric database (Version 1.0)). For the material, we used our special material.
References
Abd El-aziz AA, EL-daydamony EM, Raid AM, Soliman HH (2019) Online user authentication system based on finger knuckle print using smartphones. In: Security in smart cities: models, applications, and challenges. Springer, pp 345–364. https://doi.org/10.1007/978-3-030-01560-2_15
Anbari M, Fotouhi AM (2021) Finger knuckle print recognition for personal authentication based on relaxed local ternary pattern in an effective learning framework. Mach vis Appl 32(3):1–11. https://doi.org/10.1007/s00138-021-01178-6
Arab M, Rashidi S (2019) Finger knuckle surface print verification using gabor filter. In: 5th Iranian conference on signal processing and intelligent systems, ICSPIS 2019, pp 1–7. https://doi.org/10.1109/ICSPIS48872.2019.9066108
Attia A, Moussaoui A, Chaa M, Chahir Y (2018) Finger-knuckle-print recognition system based on featureslevel fusion of real and imaginary images. ICTACT J Image Video Process 8(4):1793–1799. https://doi.org/10.21917/ijivp.2018.0252
Attia A, Akhtar Z, Chahir Y (2020a) Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication. SIViP. https://doi.org/10.1007/s11760-020-01806-0
Attia A, Akhtar Z, Chalabi NE, Maza S, Chahir Y (2020b) Deep rule-based classifier for finger knuckle pattern recognition system. Evol Syst. https://doi.org/10.1007/s12530-020-09359-w
Attia A, Chaa M, Akhtar Z, Chahir Y (2020c) Finger kunckcle patterns based person recognition via bank of multi-scale binarized statistical texture features. Evol Syst 11(4):625–635. https://doi.org/10.1007/s12530-018-9260-x
Belhumeur PN, Hespanha JP, Kriegman DJ (1996) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 1064, vol 7, pp 45–58. https://doi.org/10.1007/bfb0015522
Chalabi NE, Attia A, Bouziane A (2020) Multimodal finger dorsal knuckle major and minor print recognition system based on PCANET deep learning. ICTACT J Image Video Process 10(3):2153–2158. https://doi.org/10.21917/ijivp.2020.0308
Chauhan S, Arora AS, Kaul A (2010) A survey of emerging biometric modalities. Procedia Comput Sci 2:213–218. https://doi.org/10.1016/j.procs.2010.11.027
Cheniti M, Boukezzoula NE, Akhtar Z (2018) Symmetric sum-based biometric score fusion. IET Biom 7(5):391–395. https://doi.org/10.1049/iet-bmt.2017.0015
Funt BV, Drew MS, Brockington M (1992) Recovering shading from color images. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), 588 LNCS, pp 124–132. https://doi.org/10.1007/3-540-55426-2_15
Gao G, Yang J, Qian J, Zhang L (2014) Integration of multiple orientation and texture information for finger-knuckle-print verification. Neurocomputing 135:180–191. https://doi.org/10.1016/j.neucom.2013.12.036
Hadid A, Ylioinas J, Lopez MB (2015). Face and texture analysis using local descriptors: a comparative analysis. In: 2014 4th international conference on image processing theory, tools and applications, IPTA 2014, pp 1–4. https://doi.org/10.1109/IPTA.2014.7001944
Hammouche R, Attia A, Akhrouf S (2020a) Descriptors enhancement using sparse autoencoder for biometric system based minor, major finger knuckle pattern. In: ISIA 2020a—Proceedings, 4th international symposium on informatics and its applications, pp 1–6. https://doi.org/10.1109/ISIA51297.2020.9416538
Hammouche R, Attia A, Akrouf S (2020b) A novel system based on phase congruency and gabor-filter bank for finger knuckle pattern authentication. ICTACT J Image Video Process 10(3):2125–2131. https://doi.org/10.21917/ijivp.2020.0303
Hanmandlu M, Grover J, Gureja A, Gupta HM (2011) Score level fusion of multimodal biometrics using triangular norms. Pattern Recognit Lett 32(14):1843–1850. https://doi.org/10.1016/j.patrec.2011.06.029
Jain AK, Ross A, Prabhakar S (2004) An Introduction to Biometric Recognition. IEEE Trans Circuits Syst Video Technol 14(1):4–20. https://doi.org/10.1109/TCSVT.2003.818349
Jain AK, Flynn, P, Ross AA (2007) Handbook of biometrics. In: Handbook of biometrics. Springer Science & Business Media. https://doi.org/10.1007/978-0-387-71041-9
Jaswal G, Nigam A, Nath R (2017) DeepKnuckle: revealing the human identity. Multim Tools Appl 76(18):18955–18984. https://doi.org/10.1007/s11042-017-4475-6
Jaswal G, Nigam A, Kaul A, Nath R, Singh AK (2019) Bring your own hand: how a single sensor is bringing multiple biometrics together. Soft Comput 23(19):9121–9139. https://doi.org/10.1007/s00500-018-03709-2
Kannala J, Rahtu E (2012) BSIF: binarized statistical image features. In: Proceedings—international conference on pattern recognition, pp 1363–1366
Kumar A (2012) Can we use minor finger knuckle images to identify humans? In: 2012 IEEE 5th international conference on biometrics: theory, applications and systems, BTAS 2012, pp 55–60. https://doi.org/10.1109/BTAS.2012.6374558
Kumar A (2014) Importance of being unique from finger dorsal patterns: exploring minor finger knuckle patterns in verifying human identities. IEEE Trans Inf Forensics Secur 9(8):1288–1298. https://doi.org/10.1109/TIFS.2014.2328869
Kumar A, Xu Z (2014) Can we use second minor finger knuckle patterns to identify humans? In: IEEE computer society conference on computer vision and pattern recognition workshops, pp 106–112. https://doi.org/10.1109/CVPRW.2014.21
Kumar A, Zhou Y (2012) Human identification using finger images. IEEE Trans Image Process 21(4):2228–2244. https://doi.org/10.1109/TIP.2011.2171697
Kumar AM, Chandralekha A, Himaja Y, Sai SM (2019) Local binary pattern based multimodal biometric recognition using ear and FKP with feature level fusion. In: IEEE international conference on intelligent techniques in control, optimization and signal processing, INCOS 2019, pp 1–5. https://doi.org/10.1109/INCOS45849.2019.8951348
Kusanagi D, Aoyama S, Ito K, Aoki T (2017) A practical person authentication system using second minor finger knuckles for door security. IPSJ Trans Comput vis Appl 9(1):8. https://doi.org/10.1186/s41074-017-0016-5
Land EH, McCann JJ (1971) Lightness and retinex theory. J Opt Soc Am 61(1):1–11. https://doi.org/10.1364/JOSA.61.000001
Mahesh Kumar NB, Premalatha K (2014) Finger knuckle-print identification based on local and global feature extraction using sdost. Am J Appl Sci 11(6):929–938. https://doi.org/10.3844/ajassp.2014.929.938
Nigam A, Tiwari K, Gupta P (2016) Multiple texture information fusion for finger-knuckle-print authentication system. Neurocomputing 188:190–205. https://doi.org/10.1016/j.neucom.2015.04.126
Park YK, Park SL, Kim JK (2008) Retinex method based on adaptive smoothing for illumination invariant face recognition. Signal Process 88(8):1929–1945. https://doi.org/10.1016/j.sigpro.2008.01.028
Rani E, Shanmugalakshmi R (2013) Finger knuckle print recognition techniques—a survey. Int J Eng Sci 2(11):62–69
Richards DR (1997) Biometric identification. Inf Syst Secur 6(2):28–44. https://doi.org/10.1080/10658989709342534
Shameem Sulthana ES, Kanmani S (2014) Implementation and evaluation of SIFT descriptors based finger-knuckle-print authentication system. Indian J Sci Technol 7(3):374–382. https://doi.org/10.17485/ijst/2014/v7i3.15
Sonawane SJ, Dhanokar G (2016) Verifying human identities using major and minor finger knuckle pattern-result analysis. Int J 1(5):305–309
Štruc V, Pavešić N (2010) The complete Gabor-fisher classifier for robust face recognition. Eurasip J Adv Signal Process 2010(1):1–26. https://doi.org/10.1155/2010/847680
Thapar D, Jaswal G, Nigam A (2019) FKIMNet: a finger dorsal image matching network comparing component (major, minor and nail) matching with holistic (finger dorsal) matching. In: Proceedings of the international joint conference on neural networks, 2019-July, pp 1–8. https://doi.org/10.1109/IJCNN.2019.8852390
The Hong Kong Polytechnic University Contactless Finger Knuckle Images Database (Version 1.0) (2006). https://www4.comp.polyu.edu.hk/~csajaykr/fn1.htm. Accessed Apr 2020
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86. https://doi.org/10.1162/jocn.1991.3.1.71
Usha K, Ezhilarasan M (2016) Personal recognition using finger knuckle shape oriented features and texture analysis. J King Saud Univ Comput Inf Sci 28(4):416–431. https://doi.org/10.1016/j.jksuci.2015.02.004
Vyas R, Rahmani H, Boswell-Challand R, Angelov P, Black S, Williams BM (2021) Robust end-to-end hand identification via holistic multi-unit knuckle recognition. In: 2021 IEEE international joint conference on biometrics, IJCB 2021, pp 1–8. https://doi.org/10.1109/IJCB52358.2021.9484356
Zhang D, Jing X, Yang J (2006) Biometric image discrimination technologies. In: Computational intelligence and its applications series. IGI Global. http://www.loc.gov/catdir/toc/ecip063/2005032048.html. Accessed Apr 2020
Zhang D, Lu G, Zhang L (2018) Finger-Knuckle-Print verification with score level adaptive binary fusion. In: Advanced biometrics. Springer, Cham. https://doi.org/10.1007/978-3-319-61545-5_8
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Hammouche, R., Attia, A. & Akhrouf, S. Score level fusion of major and minor finger knuckle patterns based symmetric sum-based rules for person authentication. Evolving Systems 13, 469–483 (2022). https://doi.org/10.1007/s12530-022-09430-8
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DOI: https://doi.org/10.1007/s12530-022-09430-8