A novel feature biometric fusion approach for iris, speech and signature

• Autorzy: Garg Mamta , Arora Ajat Shatru , Gupta Savita

Warianty tytułu

PL

Nowatorskie połączenie cech biometrycznych dla tęczówki, mowy i podpisu

• Języki publikacji: EN

Abstrakty

EN

With an ever-increasing emphasis on security and the new dimensions in security challenges facing the world today, the need for automated personal identification/verification system based on multimodal biometrics has increased. This paper addresses the issue of multiple biometric fusion to enhance the security of recognition. The paper utilizes iris, speech,and signature for the novel fusion. A segregated classification mechanism for each biometric is also presented. The fusion is done on the base of features extracted at the time of individual classification of biometrics. Different feature extraction algorithms are applied for different biometrics. The paper has utilized 2-Dimensional Principle Component Analysis (2DPCA) for Iris, Scale Invariant Feature Transform (SIFT) for signature and Mel-frequency cepstral coefficients for speech biometric. This paper utilizes Genetic Algorithm for the optimization of the evaluated features. The classification is done using Artificial Neural Network (ANN).

PL

W związku z ciągłym wzrostem wymagań dotyczących bezpieczeństwa i nowymi wyzwaniami stojącymi dzisiaj w tym zakresie przed światem istnieje potrzeba tworzenia ststremów wykorzystujących biometrię multimodalną do automatycznej identyfikacji/weryfikacji osób. Artykuł opisuje problem zastosowania multimodalnej fuzji biometrycznej do poprawy bezpieczeństwa rozpoznawania osób. Do nowej fuzji wykorzystano tęczówkę, mowę i podpis. Zaprezentowano oddzielny mechanism dla każdego czynnika biometrycznego. Fuzję przeprowadzono wykorzystując cechy wybrane w danej chwili czasu indywidualnie dla każdego czynnika. Dla różnych czynników zastosowano różny algorytm wyboru cech biometrycznych. Zastosowano 2- wymiarową analizę podstawowych składników (ang. 2-Dimensional Principle Component Analysis - 2DPCA) dla tęczówki, skaloniezmiennicze przekształcenie cech (ang. Scale Invariant Feature Transform - SIFT) dla podpisu oraz parametry melcepstralne (ang. Mel-Frequency Cepstral Coefficients) dla mowy. W artykule wykorzystano metodę Algorytmów Genetycznych do optymalizacji oceny poszczególnych cech. Klasyfikację przeprowadzono wykorzystując sztuczne sieci neuronowe.

Słowa kluczowe

EN

biometric fusion; scale invariant feature transform; 2-dimensional principle component analysis; mel-frequency cepstral coefficient; genetic algorithms; artificial neural networks

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Methods in Materials Science
• Rocznik: 2020
• Tom: Vol. 20, No. 2
• Strony: 63--71
• Opis fizyczny: Bibliogr. 25 poz., rys.

Twórcy

autor

Garg Mamta

• Department of Computer Engineering, Govt Polytechnic College For Girls, Jalandhar, India, 144001

autor

Arora Ajat Shatru

• Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering & Technology, Longowal, India, 148106

autor

Gupta Savita

• Department of Computer Science and Engineering, University Institute of Engineering & Technology, Panjab University, Chandigarh, India, 160016

Bibliografia

• Almayyan, W., Own, H., Abel-Kader, R., Zedan, H., 2011, A multimodal biometric fusion approach based on binary particle optimization,in: Research and Development in Intelligent Systems XXVIII, SGAI, Eds, Bramer, M., Petridis, M.,. Nolle, L., Springer, London, 139-152.
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• Chen, C.H., Chu, C., 2006, Fusion of face and iris features for multimodal biometrics, in: Advances in Biometrics ICB, Eds, Zhang, D., Jain, A.K,. vol. 3832, Springer, Berlin, Heidelberg, 571-580.
• Chetty, G.,. Wagner, M., 2005, Investigating feature-level fusion for checking liveness in face-voice authentication, in: Proc. 8th IEEE International Symposium on Signal Processing and its Applications, Sydney, 66-69.
• Cui, J., Wang, Y. , Huang, J. , Tan, T., Sun, Z., Ma, L. 2004, An Iris Image Synthesis Method Based on PCA and Super-Resolution, in: Proc. 17th Int. Conf. on Pattern Recognition, Cambridge, 471-474.
• Dhameliya, M.D, Chaudhari, J.P., 2013, A multimodal biometric recognition system based on fusion of palmprint and fingerprint, International Journal of Engineering Trends and Technology, 4, 1908-1911.
• Eskandari, M., Toygar, Ö., Demirel, H., 2014, Feature extractor selection for face–iris multimodal recognition, Signal, Image and Video Processing, 8(6), 1189-1198.
• Haghighat, M., Abdel-Mottaleb, M., Alhalabi, W., 2016, Discriminant correlation analysis: Real-time feature level fusion for multimodal biometric recognition, IEEE Transactions on Information Forensics and Security, 11(9), 1984-1996.
• Huang, C.L., Wang, C.J., 2006, A GA-based feature selection and parameters optimization for support vector machines, Expert Systems with Applications, 31(2), 231-240.
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• Leghari, M., Memon, S., Chandio, A.A., 2018, Feature-level fusion of fingerprint and online signature for multimodal biometrics,” in Proc: IEEE International Conference on Computing, Mathematics and Engineering Technologies, Sukkur, 1-4.
• Liau, H.F., Isa, D., 2011, Feature selection for support vector machine-based face-iris multimodal biometric system, Expert Systems with Applications, 38(9), 11105-11111.
• Malode, A.A., Sahare, S.L. , An improved speaker recognition by using VQ & HMM, in: Proc. IET Chennai 3rd International on Sustainable Energy and Intelligent Systems, Tiruchengode, 377-383.
• Nadheen, M.F., Poornima, S., 2013, Feature level fusion in multimodal biometric authentication system, International Journal of Computer Applications, 69(18), 36-40.
• Ou, G., Murphey, Y.L., 2007, Multi-class pattern classification using neural networks, Pattern Recognition, 40(1), 4-18.
• Park, G., Kim, S., 2013, Hand biometric recognition based on fused hand geometry and vascular patterns, Sensors, 13(3), 2895-2910.
• Rattani, A., Kisku, D.R., Bicego M., Tistarelli, M., 2007, Feature level fusion of face and fingerprint biometrics, in:
• Proc. First IEEE International Conference on Theory, Applications and Systems, Crystal City, 1-6.
• Rattani, A., Tistarelli, M., 2009, Robust multi-modal and multiunit feature level fusion of face and iris biometrics, in: Advances in Biometrics, Eds, Tistarelli, M., Nixon, M.S., vol. 5558, Springer, Berlin, Heidelberg, 960-969.
• Saleh, A., Alzoubiady, L.M., 2014, Decision level fusion of iris and signature biometrics for personal identification using ant colony optimization, International Journal of Engineering and Innovative Technology, 3(11), 35-42.
• Sarhan, S., Alhassan, S., Elmougy, S., 2017, Multimodal biometric systems: a comparative study, Arabian Journal For Science And Engineering, 42(2), 443-457.
• Supreetha Gowda, H.D., Hemantha Kumar, G., Imran, M., 2018, Multi-modal biometric system on various levels of fusion using LPQ features, Journal of Information and Optimization Sciences, 39(1), 169-181.
• Veluchamy, S., Karlmarx, L.R., 2016, System for multimodal biometric recognition based on finger knuckle and finger vein using feature-level fusion and k-support vector machine classifier, IET Biometrics, 6(3), 232-242.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).