A Robust Multi-Modal Biometric Recognition System Using Iris, Fingerprint and Palmprint based on Cuckoo Search Algorithm

Abstract: Authentication enables individuals to be automatically recognized based on their behavioral or physiological traits. Biometrics is extensively utilized in many commercial and official identifying systems to facilitate automated access control. This research presents a model for multimodal biometric recognition that utilizes a feature level fusion method. The suggested method encompasses a series of five processes, namely pre-processing, feature extraction from all attributes, feature level fusion, feature space reduction, and recognition via machine learning techniques. The initial stage involves the pre-processing of three distinct modalities, namely iris, pamprint, and fingerprint. Next, the process of feature extraction is conducted for each modality in order to extract the features. Following this, the features extracted from three modalities were combined at the feature level. The utilization of feature level fusion in integrating multiple biometric data presents several advantages in comparison to alternative fusion procedures, but accompanied by the notable limitation of creating feature vectors of substantial dimensions. The main objective of this study is to analyze the difficulties related to the management of high-dimensional data and investigate several methods of feature reduction that can be applied to multimodal biometric systems.

This study presents a novel approach that employs Cuckoo Search (CS) optimization technique for the purpose of feature selection. The objective is to address the challenges related to integrating the Iris, palmprint, and fingerprint feature spaces at the feature level. Normalization is applied to bring all the feature spaces into same domain during integration of features at feature level. Machine Learning approaches are utilized to assess the effectiveness of feature selection based on Cuckoo Search Algorithm (CSA) and feature space reduction using Principal Component Analysis (PCA) on the CASIA, IITD, and FVC databases. Additionally, matching is performed using the Euclidean distance. The trials undertaken in this study indicated a significant reduction in the feature space when iris, palmprint, and fingerprint characteristics were merged at the feature level. Specifically, the use of CS resulted in a greater reduction compared to PCA. The decrease in size led to an improvement in the accuracy of recognition.

Cuckoo Search optimization, Multi-modal biometric systems Feature Level Fusion, Palmprint, Iris, Fingerprint, Feature selection

1. Milad Salem, Shayan Taheri and Jiann-Shiun Yuan,” Utilizing transfer learning and homomorphic encryption in a privacy preserving and secure biometric recognition system”, vol. 8, no. 1, pp. 3, 2019. [Google Scholar] [Crossref]

2. Soliman Randa F, Amin Mohamed, Fathi E. Abd El-Samie, “A Novel Cancelable Iris Recognition Approach novel cancelable Iris recognition system based on feature learning techniques. Inf Sci 2017;406:102–18. [Google Scholar] [Crossref]

3. Powalkar Samarjeet, Mukhedkar Moresh M. Fast face recognition based on wavelet transform on pca. Int J Sci Res Sci Eng Technol (IJSRSET) 2015;1(4):21–4. [Google Scholar] [Crossref]

4. Al‑Waisy Alaa S, Qahwaji Rami, Ipson Stanley, Al‑Fahdawi Shumoos, Nagem Tarek AM. A multi‑biometric iris recognition system based on a deep learning approach. Pattern Anal Appl August 2018;21(3):783–802. [Google Scholar] [Crossref]

5. Brammya G, Suki Antely A. Face recognition using active appearance and type-2 fuzzy classifier. Multimedia Res 2019;2(1). [Google Scholar] [Crossref]

6. Lee Eui Chul, Jung Hyunwoo, Kim Daeyeoul. New finger biometric method using near infrared imaging. Sensors 2011. [Google Scholar] [Crossref]

7. Ninu Preetha NS, Brammya G, Ramya R, Praveena S, Binu D, Rajakumar BR. Grey wolf optimisation-based feature selection and classification for facial emotion recognition. IET Biometrics 2018;7(5):490–9. [Google Scholar] [Crossref]

8. Gupta Richa, Sehgal Priti. A complete end-to-end system for iris recognition to mitigate replay and template attack. Soft Comput Signal Process 2019:571–82. [Google Scholar] [Crossref]

9. Othman N, Dorizzi B. Impact of quality-based fusion techniques for video-based iris recognition at a distance. IEEE Trans Inf Forensics Secur 2015;10(8):1590–602. [Google Scholar] [Crossref]

10. [10] Shubhika Ranjan S, Swarnalatha P, Magesh G, Sundararajan Ravee. Iris recognition system. Int Res J Eng Technol (IRJET) December 2017;4(12). 2395-0056. [Google Scholar] [Crossref]

11. Bowyer Kevin W, Hollingsworth Karen, Flynn Patrick J. Image understanding for iris biometrics: a survey. Comput Vision Image Understanding 2008;110:281–307. [Google Scholar] [Crossref]

12. Hofbauer Heinz, Jalilian Ehsaneddin, Uhl Andreas. Exploiting superior CNN-based iris segmentation for better recognition accuracy. Pattern Recognit Lett 2019;120: 17–23. [Google Scholar] [Crossref]

13. Maryam Mostafa Salah, Sameh A. Napoleon, El-Sayed M. El-Rabaie, Fathi E. Abd El-Samie, and Mustafa M. Abd Elnaby, "Sensitivity analysis of a class of Iris localization algorithms to blurring effect", Wirel Pers Commun, vol. 104, no. 1,pp. 269-286. [Google Scholar] [Crossref]

14. Ratre Avinash, Pankajakshan Vinod. Tucker visual search-based hybrid tracking model and fractional Kohonen self-organizing map for anomaly localization and detection in surveillance videos. Imaging Sci J 2018;66(4):195–210. [Google Scholar] [Crossref]

15. Caiyong Wang, Yuhao Zhu, Yunfan Liu, Ran He, and Zhenan Sun, Joint Iris segmentation and localization using deep multi-task learning framework, 2019. [Google Scholar] [Crossref]

16. Yue-Tong Luo, Lan-Ying Zhao, Bob Zhang, Wei Jia, Feng Xue, Jing-Ting Lu, Yi-Hai Zhu, Bing-Qing Xu, Local line directional pattern for palmprint recognition, Pattern Recognition, Volume 50, 2016, pp. 26-44. [Google Scholar] [Crossref]

17. Z. Yang, L. Leng and W. Min, "Extreme Downsampling and Joint Feature for Coding-Based Palmprint Recognition," in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-12, 2021, Art no. 5005112, doi: 10.1109/TIM.2020.3038229. [Google Scholar] [Crossref]

18. Tsai Chung-Chih, Lin Heng-Yi, Taur Jinshiuh, Tao Chin-Wang. Iris recognition using possibilistic fuzzy matching on local features. IEEE Trans Syst Man Cybern—part B: Cybernetics February 2012;42(1). [Google Scholar] [Crossref]

19. J. Daugman, High confidence visual recognition of person by a test of statistical independence, IEEE Transaction on Pattern Analysis and Machine Intelligence 15(11) (1993) 1148-1161. [Google Scholar] [Crossref]

20. W. Boles, B. Boshash, “A Human Identification Technique Using Images of the Iris and Wavelet Transform” IEEE Transactions on signal processing, vol. 46, no. 4, 1998. [Google Scholar] [Crossref]

21. Wildes R, Iris Recognition an emerging biometric technology, Proceedings of the IEEE, 85(9) (1997) 1348- 1363. [Google Scholar] [Crossref]

22. N Singh, D Gandhi, K. P. Singh, “Iris recognition using Canny edge detection and circuler Hough transform,” International Journal of Advances in Engineering & Technology, May 2011. [Google Scholar] [Crossref]

23. Lim, S., Lee, K., Byeon, O., Kim, T, “Efficient Iris Recognition through Improvement of Feature Vector and Classifier”, ETRI Journal 23(2), June 2001, pp. 61-70. [Google Scholar] [Crossref]

24. A Ross, K Nandakumar, A K Jain, Hand Book of Multibiometrics, Springer Verlag edition, 2006. [Google Scholar] [Crossref]

25. O.T. Adedeji, A.S. Falohun, O.M. Alade, E.O. Omidiora, S.O. Olabiyisi, “ Clonal Selection Algorithm for feature level fusion of multibiometric systems” Annals Comput. Sci. Series, 17 (1) (2019), pp. 69-77. [Google Scholar] [Crossref]

26. O.T. Adedeji, A.S. Falohun, O.M. Alade, E.O. Omidiora, S.O. Olabiyisi, “Comparative analysis of face, Iris and fingerprint recognition systems” World J.f Eng. Res. Technol. (WJERT0, 4 (4) (2018), pp. 160-167. [Google Scholar] [Crossref]

27. V. Kumar, S. Minz, “Feature selection: a literature review”, Smart Comput. Rev., 4 (1) (2014), pp. 211-229. [Google Scholar] [Crossref]

28. V. Rajasekar, B. Predić, M. Saračević, M. Elhoseny, D. Karabasevic, D. Stanujkic, P. Jayapaul, “ Enhanced multimodal biometric recognition approach for smart cities based on an optimized fuzzy genetic algorithm Sci. Rep., 12 (2022), p. 622. [Google Scholar] [Crossref]

29. U. Park, S. Pankanti, A. K. Jain, Fingerprint Verification using SIFT features, Proceedings of SPIE Defense and Security Symposium, pp. 69440K-69440K-9 (2008). [Google Scholar] [Crossref]

30. Y. S. Moon, H. W. Yeung, K. C. Chan, S. O. Chan, Template synthesis and image mosaicking for fingerprint registration: an experimental study, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Proceedings 2004 (ICASSP‟04) vol.5, pp. 409-412, 2004. [Google Scholar] [Crossref]

31. Faundez-Zanuy M, Data Fusion in biometrics, In IEEE Aerospace and Electronic Systems Magzine, 20 (2005) 34-48. [Google Scholar] [Crossref]

32. Chen. Y, Li. Y, Cheng. X, Guo. L, Survey and Taxonomy of Feature Selection Algorithms in Intrusion Detection System, In Lipmaa H., Yung M., Lin D. (eds) Information Security and Cryptology. Inscrypt 2006. Lecture Notes in Computer Science, vol 4318. Springer, Berlin, Heidelberg. [Google Scholar] [Crossref]

33. JX. Shi, XF. Gu, The comparison of iris recognition using Principal Component Analysis, Independent Component Analysis and Gabor Wavelets, IEEE, International Conference on Computer Science and Information Technology,2010. [Google Scholar] [Crossref]

34. G. Feng, K. Dong, D. Hu, D. Zhang, When faces are combined with palmprints: a novel biometric fusion strategy, in: First International Conference on Biometric Authentication (ICBA), 2004, pp.701-707. [Google Scholar] [Crossref]

35. Y. Yan, Y.J. Zhang, Multimodal biometrics fusion using correlation filter bank, in: proceedings of International conference on Pattern Recognition (ICPR-2008), 2008, pp.1-4. [Google Scholar] [Crossref]

36. Y. Yao, X. jing, H. Wong, Face and palmprint feature level fusion for single sample biometric recognition, Neurocomputing 70(7-9) (2007) 1582-1586. [Google Scholar] [Crossref]

37. A. Ross, R. Govindarajan, Feature level fusion using Hand and Face biometrics, Proceedings of SPIE Conference on biometric technology for human identification II, Orlando, USA, pp. 196-204, March 2005. [Google Scholar] [Crossref]

38. A. Rattani, D.R. Kisku, M. Bicego, Feature level fusion of face and fingerprint biometrics, in: Proceedings of First IEEE international Conference on Biometrics: Theory, Applications, and Systems (BTAS 2007), pp. 1-6, 2007. [Google Scholar] [Crossref]

39. S. Singh, G. Gyaourova and I. Pavlidis, Infrared and visible image fusion for face recognition, SPIE Defence and Security Symposium, pp. 585-596,2004. [Google Scholar] [Crossref]

40. A.A. Altun, H.E. Kocer, N. Allahverdi, Genetic algorithm based feature selection level fusion using fingerprint and iris biometrics, International Journal of Pattern Recognition and Artificial intelligence (IJPRAI) 22(3) (2008) 585-600. [Google Scholar] [Crossref]

41. A. Draa, A. Bouaziz, An Artificial Bee Colony algorithm for image contrast enhancement, J.SwarmEvolut.Comput.16(2014)69–84. [Google Scholar] [Crossref]

42. S. Saadi, A. Guessouma, M. Bettayeb, ABC optimized neural network model for image deblurring with its FPGA implementation, Microprocess, Microsyst, 37 (2013) 52-64. [Google Scholar] [Crossref]

43. A. Bouaziz, et al., Artificial bees for multilevel thresholding of iris images, Swarm and Evolutionary Computation, 21 (2015) 32-40. [Google Scholar] [Crossref]

44. C. Ozturk, D. Karaboga, Hybrid Artificial Bee Colony algorithm for neural network training, in: IEEE Congress on Evolutionary Computation (CEC), 2011, pp. 84–88. [Google Scholar] [Crossref]

45. R. Srinivasa Rao, S. V. L. Narasimham, M. Ramalinga raju, Optimization of distribution network configuration for loss reduction using Artificial Bee Colony algorithm, International Journal of Electrical Power Energy System Engineering, 1(2)(2008)644–650. [Google Scholar] [Crossref]

46. Ramesh Jain, Rangachar Kasturi, Brian G Schunck, Machine Vision, McGraw-Hill,1995. [Google Scholar] [Crossref]

47. Vanaja Roselin.E.C, L.M.Waghmare, Pupil detection and feature extraction algorithm for Iris recognition, AMO-Advanced Modeling and Optimization, Volume 15, Number 2, 2013. [Google Scholar] [Crossref]

48. A. Kong, D. Zhang, and M. Kamel, “A survey of palmprint recognition,” Pattern Recognition Letters, vol. 42, pp. 1408–1418, 2009. [Google Scholar] [Crossref]

49. Alice Nithya A, Lakshmi C, Feature Extraction Techniques For Recognition of Iris images: A Review, International Journal of Control Theory and Applications (IJCTA), 9(28) 2016, pp.87-92. [Google Scholar] [Crossref]

50. P. Aruna Kumari, G. Jaya Suma, An Experimental Study of Feature Reduction Using PCA in Multi-Biometric Systems Based on Feature Level Fusion, 2016 International Conference on Advances in Electrical, Electronic and System Engineering, 14-16 Nov 2016, Putrajaya, Malaysia. [Google Scholar] [Crossref]

51. H. Mehrotra, B. Majhi, Phalguni Gupta, “Multi-algorithmic Iris Authentication System”, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol.2, No.8, 2008. [Google Scholar] [Crossref]

52. Letian Cao, Yazhou Wang, Fingerprint image enhancement and minutiae extraction algorithm, 2016. [Google Scholar] [Crossref]

53. M. F. Fahmy, M. A. Thabet, A Fingerprint Segmentation Technique Based on Morphological Processing, ISSPIT, 2013. [Google Scholar] [Crossref]

54. T. Y. Zhang, C. Y. Suen, A Fast Parallel Algorithm for Thinning Digital Patterns, Image Processing and Computer Vision, 27(3) (1984) 236-239. [Google Scholar] [Crossref]

55. Stentiford. F. W. M, Mortimer. R. G, Some new heuristics for thinning binary handprinted characters for OCR, IEEE Transactions on Systems, Man, and Cybernetics, SMC-13(1) (1983) 81-84. [Google Scholar] [Crossref]

56. D. Rutovitz, Pattern recognition, J. Roy. Stat. Soc. 129 (1966) 504–530. [Google Scholar] [Crossref]

57. Jamal Hussain Shah, Muhammad Sharif, Mudassar Raza, and Aisha Azeem, A Survey: Linear and Nonlinear PCA Based Face Recognition Techniques, The International Arab Journal of Information Technology, Vol. 10, No. 6, November 2013. [Google Scholar] [Crossref]

58. Mithuna Behera et al,Palm print Authentication Using PCA Technique, International Journal of Computer Science and Information Technologies, Vol. 5 (3), 2014, 3638-3640. [Google Scholar] [Crossref]

59. Rohit Khokher, Ram Chandra Singh, Rahul Kumar, Footprint Recognition with Principal Component Analysis and Independent Component Analysis, Macromol. Symp. 2015, 347, 16–26. [Google Scholar] [Crossref]

60. Nittaya Kerdprasop, Ratiporn Chanklan, Anusara Hirunyawanakul, Kittisak Kerdprasop, An Empirical Study of Dimensionality Reduction Methods for Biometric Recognition, 7th International Conference on Security Technology IEEE 2014 26-29. [Google Scholar] [Crossref]

61. Z. Wang and X. Li, Face Recognition Based on Improved PCA Reconstruction, in Intelligent Control and Automation (WCICA), 2010 8th World Congress on, 2010, pp. 6272-6276. [Google Scholar] [Crossref]

62. J. Meng and Y. Yang, Symmetrical Two-Dimensional PCA with Image Measures in Face Recognition, Int J Adv Robotic Sy, Vol. 9, 2012. [Google Scholar] [Crossref]

63. X. S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press, 2nd edition, 2010. [Google Scholar] [Crossref]

64. Yang, Xin-She. "Cuckoo Search and Firefly Algorithm: Overview and Analysis." Cuckoo Search and Firefly Algorithm. Springer International Publishing, 2014. 1-26. [Google Scholar] [Crossref]

65. H. K. Kwan and J. Liang, "Minimax design of linear phase FIR filters using cuckoo search algorithm", in Proceedings of 8th International Conference on Wireless Communications and Signal Processing (WCSP 2016), Yangzhou, Jiangsu, China, October 13-15, 2016, pp.1-4. [Google Scholar] [Crossref]

66. CASIA IrisV1, http://biometrics.idealtest.org/ [Google Scholar] [Crossref]

67. http://web.iitd.ac.in/~biometrics/Databasse_Iris.htm [Google Scholar] [Crossref]

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