a survey on multi-biometric fusion approaches

Ayash, Hawraa Abed Alkareem Hussain; Abbas, Hawraa Hassan

	a survey on multi-biometric fusion approaches
Kerbala Journal for Engineering Sciences
Volume 3, Issue 2, June 2023, Pages 79-100 PDF (1.35 M)
Document Type: Review Article
Authors
hawraa Abed alkareem hussain Ayash^* ¹; hawraa hassan abbas²
¹Computer science.babylon university .
²Department of Electrical & Electronic Engineering ,College of Engineering, University of kerbala, kerbala, iraq.
Abstract
The goal of biometrics is to reliably and robustly identify people based on their unique personal characteristics, primarily for security and authentication needs, but also to identify and track the users of more intelligent applications. Fingerprints, iris, palm print, face and voices are frequently used modalities, but there are numerous more potential biometrics, such as stride, ear image, retina, DNA, and even behavior. As an automatic way to identify persons depend on just one (single modal biometrics) or a mix of (multi-modal biometrics). A fusion of two or more photos can be utilized to create multimodal biometrics, and the resulting fused image will be more secure. Several levels of fusion techniques are currently accessible, including feature level, decision level, matching score level, etc. In order to identify the human biometric by extracting features and classifying images, this paper discusses different fusion approaches that are implemented in multimodal biometrics. It also describes the datasets that were used and the outcomes ,results and conclusions that were obtained. The goal of biometrics is to reliably and robustly identify people based on their unique personal characteristics, primarily for security and authentication needs, but also to identify and track the users of more intelligent applications. Fingerprints, iris, palm print, face and voices are frequently used modalities, but there are numerous more potential biometrics, such as stride, ear image, retina, DNA, and even behavior. As an automatic way to identify persons depend on just one (single modal biometrics) or a mix of (multi-modal biometrics). A fusion of two or more photos can be utilized to create multimodal biometrics, and the resulting fused image will be more secure. Several levels of fusion techniques are currently accessible, including feature level, decision level, matching score level, etc. In order to identify the human biometric by extracting features and classifying images, this paper discusses different fusion approaches that are implemented in multimodal biometrics. It also describes the datasets that were used and the outcomes ,results and conclusions that were obtained.
Keywords
biometric fusion; Deep Learning; Convolutional Neural Networks; recognition system; multimodal biometric system

References
[1] A. Kamboj, R. Rani, and A. Nigam, “A comprehensive survey and deep learning-based approach for human recognition using ear biometric,” Visual Computer. 2021. doi: 10.1007/s00371-021-02119-0. [2] K. Nandakumar, A. Ross, and A. K. Jain, “Biometric_fusion_Does_modeling_correlati.pdf.” 2009. [3] K. Ricanek, P. N. Belhumeur, and T. E. Boult, “must be obtained for all other uses , in any current or future media , including Fusing with Context : a Bayesian Approach to Combining Descriptive Attributes.” 2011. [4] D. Chang, S. Garg, M. Ghosh, and M. Hasan, “BIOFUSE: A framework for multi-biometric fusion on biocryptosystem level.pdf.” 2020. [5] K. Saeed et al., “A Novel Extension for e-Safety Initiative Based on Developed Fusion of Biometric Traits.pdf.” 2020. [6] L. Di Martino, G. Garella, J. Preciozzi, A. Fernandez, F. Lecumberry, and R. G. von Gioi, “An a-contrario Biometric Fusion Approach .pdf.” 2020. [7] S. K. Choudhary and A. K. Naik, “Multimodal biometric authentication with secured templates – A review,” Proceedings of the International Conference on Trends in Electronics and Informatics, ICOEI 2019, vol. 2019-April. pp. 1062–1069, 2019. doi: 10.1109/icoei.2019.8862563. [8] M. Singh, R. Singh, and A. Ross, “A comprehensive overview of biometric fusion.pdf.” 2019. [9] S. Bharadwaj, M. Vatsa, and R. Singh, “Aiding face recognition with social context association rule based re-ranking,” IJCB 2014 - 2014 IEEE/IAPR International Joint Conference on Biometrics. 2014. doi: 10.1109/BTAS.2014.6996266. [10] R. Singh, M. Vatsa, A. Ross, and A. Noore, “A Mosaicing Scheme for Pose-Invariant Face Recognition.pdf.” 2007. [11] A. Ross, S. Shah, and J. Shah, “Image versus feature mosaicing: a case study in fingerprints.pdf.” 2006. [12] A. Ross, R. Govindarajan, and Arun, “Feature level fusion of hand and face biometrics.pdf.” 2005. [13] D. Yi, Z. Lei, and S. Z. Li, “Shared representation learning for heterogenous face recognition,” 2015 11th IEEE Int. Conf. Work. Autom. Face Gesture Recognition, FG 2015, no. April, 2015, doi: 10.1109/FG.2015.7163093. [14] N. Poh, J. Kittler, and T. Bourlai, “Improving biometric device interoperability by likelihood ratio-based quality dependent score normalization,” IEEE Conference on Biometrics: Theory, Applications and Systems, BTAS’07. 2007. doi: 10.1109/BTAS.2007.4401964. [15] K. Veeramachaneni, L. Osadciw, A. Ross, and N. Srinivas, “Decision-level fusion strategies for correlated biometric classifiers,” 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops. 2008. doi: Vol. xx, No. xx ( 2021 ) ISSN: 2709-6718 10.1109/CVPRW.2008.4563104. [16] A. J. M. Indovina, U. Uludag, R. Snelick, A. Mink, “Multimodal Biometric Authentication Methods: A COTS Approach M.pdf.” 2003. [17] F. Shaheen, B. Verma, and M. Asafuddoula, ““Impact of Automatic Feature Extraction in Deep Learning Architecture,” Int. Conf. Digit. Image Comput. Tech. Appl., pp. 1–8, 2016, doi: 10.1109/DICTA.2016.7797053. [18] J. Schmidhuber, “Deep Learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85–117, 2015, doi: 10.1016/j.neunet.2014.09.003. [19] X. SERRA, “Face recognition using Deep Learning,” Polytech. Univ. Catalonia, 2017. [20] and Y. W. T. G. E. Hinton, S. Osindero, “A fast learning algorithm for deep belief nets,” Neural Comput., 2006. [21] L. D. and S. Dongmei, “Contactless palmprint recognition based on convolutional neural network,” IEEE 13th Int. Conf. Signal Process., 2016. [22] L. A. et Al Alzubaidi, “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J. Big Data, vol. 8, no. 1, Dec., 2021, doi: 10.1186/s40537-021-00444-8. [23] M. Paliwal and U. A. Kumar, “Neural networks and statistical techniques: A review of applications,” Expert Systems with Applications, vol. 36, no. 1. pp. 2–17, 2009. doi: 10.1016/j.eswa.2007.10.005. [24] N. Alay and H. H. Al-Baity, “Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits,” Sensors, vol. 20, no. 19, p. 5523, Sep. 2020, doi: 10.3390/s20195523. [25] M. GARG, A. S. ARORA, and S. GUPTA, “A NOVEL FEATURE BIOMETRIC FUSION APPROACH FOR IRIS, SPEECH AND SIGNATURE MAMTA.Pdf.” 2020. [26] S. Sengar, U. Hariharan, and K. Rajkumar, “Multimodal Biometric Authentication System using Deep Learning Method Sandeep.pdf.” 2020. [27] R. Vinothkanna* and A. Wahi, “A multimodal biometric approach for the recognition of finger print, palm print and hand vein using fuzzy vault.pdf.” 2020, 2020. [28] R. O. Mahmoud, M. M. Selim, and O. A. Muhi, “Fusion Time Reduction of a Feature Level Based Multimodal Biometric Authentication.pdf.” 2020. [29] xinman zhang, C. Dongxu, P. Jia, D. Yixuan, and X. Xuand, “An Efficient Android-Based Multimodal Biometric Authentication System with Face and Voice.pdf.” 2020. [30] H. Abderrahmane, G. Noubeil, Z. Lahcene, Z. Akhtar, and D. Dasgupta, “Weighted quasi-arithmetic mean based score level.pdf.” 2020. [31] M. E. Rane and U. S. Bhadade, “Multimodal score level fusion for recognition using face and palmprint,” International Journal of Electrical Engineering Education. 2020. doi: 10.1177/0020720920929662. Vol. xx, No. xx ( 2021 ) ISSN: 2709-6718 [32] R. Srivastava, “Score-Level Multimodal Biometric Authentication of Humans Using Retina, Fingerprint, and Fingervein,” International Journal of Applied Evolutionary Computation, vol. 11, no. 3. pp. 20–30, 2020. doi: 10.4018/ijaec.2020070102. [33] D. V. R. Devi and K. N. Rao, “Decision level fusion schemes for a Multimodal Biometric System using local and global wavelet features.pdf.” 2020. [34] O. N. Iloanusi and U. C. Ejiogu, “Gender classification from fused multi-fingerprint types,” Information Security Journal, vol. 29, no. 5. pp. 209–219, 2020. doi: 10.1080/19393555.2020.1741742. [35] C. Zhou, J. Huang, F. Yang, and Y. Liu, “A hybrid fusion model of iris, palm vein and finger vein for multi-biometric recognition system,” Multimedia Tools and Applications, vol. 79, no. 39–40. pp. 29021–29042, 2020. doi: 10.1007/s11042-020-08914-6. [36] M. S. Wajid, S. G. Kumar, N. Baishwar, A. Singh, and P. Verma, “Palm and Fingerprint Based Multimodal Biometric Technique.pdf.” 2020. [37] A. K. Yadav, “Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Fingerprint and Hand Written Signature Traits.Pdf.” 2021. [38] C. Kamlaskar and and A. Abhyankar, “Iris-Fingerprint multimodal biometric system based on optimal feature level fusion model.pdf.” AIMS Electronics and Electrical Engineering, 5(4): 229–250., 2021. [39] T. Kumar, S. Bhushan, and S. Jangra, “An Improved Biometric Fusion System of Fingerprint and Face using Whale Optimization .Pdf.” 2021. [40] T. Vijayakumar, “Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature,” J. Innov. Image Process., vol. 3, no. 2, pp. 131–143, 2021, doi: 10.36548/jiip.2021.2.005. [41] B. Arjun and H. N. Prakash, “Multimodal Biometric Recognition System Using Face and Finger Vein Biometric Traits with Feature and Decision Level Fusion Techniques.pdf.” 2021. [42] Channegowda a. b. and H. N. Prakash, “Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learnin... Indonesian.pdf.” 2021. [43] P. P. Sarangi, D. R. Nayak, M. Panda, and B. Majhi, “A feature-level fusion based improved multimodal biometric recognition system using ear and profile face,” Journal of Ambient Intelligence and Humanized Computing, vol. 13, no. 4. pp. 1867–1898, 2021. doi: 10.1007/s12652-021-02952-0. [44] H. Purohit and P. K. Ajmera, “Optimal feature level fusion for secured human authentication in multimodal biometric system,” Machine Vision and Applications, vol. 32, no. 1. 2021. doi: 10.1007/s00138-020-01146-6. [45] G. Jaswal and R. C. Poonia, “Selection of optimized features for fusion of palm print and finger knuckle-based person authentication,” Expert Systems, vol. 38, no. 1. 2021. doi: 10.1111/exsy.12523. [46] S. Tharewal et al., “Score-Level Fusion of 3D Face and 3D Ear for Multimodal Biometric Vol. xx, No. xx ( 2021 ) ISSN: 2709-6718 Human Recognition Sumegh.pdf.” 2022. [47] B. O. Mohammed, H. D. Majeed, S. Z. M. Hashim, and M. S. Al-Ani, “New Feature-level Algorithm for a Face-fingerprint Integral Multi- biometrics Identification System.pdf.” 2022. [48] A. A. Joseph et al., “Person Verification Based on Multimodal Biometric Recognition Annie.pdf.” 2022. [49] & S. Banati, U., Prakash, V., Verma, R., Soft Biometrics and Deep Learning: Detecting Facial Soft Biometrics Features Using Ocular and Forehead Region for Masked Face Images. 2022.
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