Integrating Face and the Both Irises for Personal Authentication

Full Text (PDF, 1240KB), PP.8-17

Views: 0 Downloads: 0

Author(s)

Leila Zoubida 1,* Reda Adjoudj 1

1. EEDIS Laboratory, Djillali Liabes University of Sidi Bel Abbes, Sidi Bel Abbes, 22000, Algeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2017.03.02

Received: 11 May 2016 / Revised: 20 Sep. 2016 / Accepted: 15 Nov. 2016 / Published: 8 Mar. 2017

Index Terms

Multibiometric, Pattern Recognition, Iris Authentication, Face Authentication, Score Level Fusion, Support Vector Machines

Abstract

The biometric authentication, which use the characteristic of persons to verify their identity by using their behavioral and physiological characteristics are an important application of the pattern recognition. There are different biometric modalities used to achieve the task of recognition. Among the most popular traits biometric currently used in several applications are the face and the iris. This paper proposes a multi-biometric technique which combines the face modality with the both irises (the left and the right irises) to authenticate the persons. The fusion of these two traits biometrics combines the advantages of the both instances of the iris modality with the face modality. The wavelets are used for the extraction of the biometrics features and the Support Vector Machine is used to obtain scores for fusion. Then, the Min-Max operator is used to normalize these scores. The fusion is operated at score level by the combination of two methods: a combination method and a classification method. So, we used the five rules (Sum, Product, Max, Min, Mean) combined with a classification method for the fusion. The Fusion is tested using the SDUMLA-HMT database. The experimental results show that multi-biometric systems achieve the task of recognition better than the mono-modal systems.

Cite This Paper

Leila Zoubida, Réda Adjoudj,"Integrating Face and the Both Irises for Personal Authentication", International Journal of Intelligent Systems and Applications (IJISA), Vol.9, No.3, pp.8-17, 2017. DOI:10.5815/ijisa.2017.03.02

Reference

[1]A. Ross and A. k. Jain, ”Multimodal Biometrics: An Overview”, in proceedings of 12 the European Signal Processing Conference (EUSIPCO), Vienna, Austria, pp. 1221–1224, Sep. 2004.
[2]V. K. N. Kumar and B. Srinivasan, ”New Biometric Approaches for Improved Person Identification Using Facial Detection”, International Journal on Image, Graphics and Signal Processing, vol. 4, no. 8, pp. 43-49, Aug. 2012.
[3]V. K. N. Kumar, ”Performance of Personal Identification System Technique Using Iris Biometrics Technology”, International Journal on Image, Graphics and Signal Processing, vol. 5, no. 5, pp. 63-71, Apr. 2013.
[4]V. R. E. Chirchi and L. M. Waghmare, ”Iris Biometric Authentication used for Security Systems”, International Journal of Image, Graphics and Signal Processing, vol. 6, no. 9, pp. 54-60, Aug. 2014.
[5]Z. Leila and A. Réda, ”Multibiometric Fusion: Left and Right Iris Based Authentication Technique”, to be appear in IJIGSP Journal, MECS Press.
[6]A. Rattani and M. Tistarelli, ”Robust Multimodal and Multiunit Feature Level Fusion of Face and Iris Biometrics”, In International Conference on Biometrics, pp. 960–969, Springer Berlin Heidelberg, Jun. 2009.
[7]B. Son and Y. Lee, ”Biometric Authentication System Using Reduced Joint Feature Vector of Iris and Face”, International Conference on Audio and Video-Based Biometric Person Authentication(AVBPA 2005), pp. 513-522, Springer Berlin Heidelberg, 2005.
[8]J. Lin, J. P. Li, H. Lin, and J. Ming, “Robust Person Identification with Face and Iris by Modified PUM Method”, International Conference on Apperceiving Computing and Intelligence Analysis (ICACIA 2009), pp. 321–324, Oct .2009.
[9]J. Y. Gan, J. H. Gao, and J. F. Liu, “Research on Face and Iris Feature Recognition Based on 2DDCT and Kernel Fisher Discriminant Analysis”, International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR 2008), vol. 1, pp. 401–405, Aug. 2008.
[10]J. Y. Gan and J. F. Liu, “Fusion and Recognition of Face and Iris Feature Based on Wavelet Feature and KFDA”, International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR 2009), pp. 47–50, Jul. 2009.
[11]K. Fakhar, M. El Aroussi, R. Saadane, M. Wahbi, and D. Aboutajdine, “Fusion of Face and Iris Features Extraction Based on Steerable Pyramid Representation for Multimodal Biometrics”, International Conference on Multimedia Computing and Systems (ICMCS 2011), pp. 1–4, Jul. 2011.
[12]C. H. Chen and C. Te Chu, “Fusion of Face and Iris Features for Multimodal Biometrics”, International Conference on Biometrics, pp. 571–580. Springer Berlin Heidelberg, 2006.
[13]Z. Wang, E. Wang, S. Wang, and Q. Ding, ”Multimodal Biometric System Using Face-Iris Fusion Feature”, Journal Of Computers, vol. 6, no. 5, pp. 931-938, May. 2011.
[14]H. B. Kekre, V. A. Bharadi, V. I. Singh, V. Kaul, and B Nemade, “Hybrid Multimodal Biometric Recognition using Kekre’s Wavelets, 1D Transforms & Kekre’s Vector Quantization Algorithms Based Feature Extraction of Face & Iris”, International Journal of Computer Applications (IJCA), Special Issue for ACM International Conference ICWET, pp. 29-34, 2011.
[15]D. Sharma and A. Kumar, ”An Empirical Analysis Over the Four Different Feature-Based Face and Iris Biometric Recognition Techniques“, International Journal of Advanced Computer Science and Applications (IJACSA), vol. 3, no. 10, pp. 76-83, 2012.
[16]S. A. H. Nair, P. Aruna, and M. Vadivukarassi, “PCA Based Image Fusion of Face and Iris Biometric Features”, International Journal on Advanced Computer Theory and Engineering (IJACTE), vol. 1, no. 2, pp. 106-112, 2013.
[17]Q. Wang, B. Zhu, Y. Liu, L. Xie, and Y. Zheng, ”Iris-Face Fusion and Security Analysis Based on Fisher Discriminant”, International Journal On Smart Sensing and Intelligent Systems, vol. 8, no. 1,pp. 387-407, Mar. 2015.
[18]S. Abuguba, M. M. Milosavljević and N. Maček, “An Efficient Approach to Generating Cryptographic Keys from Face and Iris Biometrics Fused at the Feature Level”, International Journal of Computer Science and Network Security (IJCSNS), vol. 15, no. 6, pp. 6-11, Jun. 2015.
[19]Z. Zhang, R. Wang, K. Pan, S. Z. Li, and P. Zhang, “Fusion of Near Infrared Face and Iris Biometrics”, International Conference on Biometrics, vol. 4642, pp. 172–180. Springer Berlin Heidelberg, Aug. 2007.
[20]Y. Wang, T. Tan, and A. K. Jain, “Combining Face and Iris Biometrics for Identity Verification”, International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA’03), pp. 805–813, Springer Berlin Heidelberg, Jun. 2003.
[21]N. Morizet and J. Gilles, ”A New Adaptive Combination Approach to Score Level Fusion for Face and Iris Biometrics Combining Wavelets and Statistical Moments”, Proceedings of the 4th International Symposium on Visual Computing (ISVC ’08), pp. 661–671, Springer Berlin Heidelberg, 2008.
[22]X. Zhang, Z. Sun, and T. Tan, “Hierarchical Fusion of Face and Iris for Personal Identification”, 20th International Conference on Pattern Recognition (ICPR 2010), pp. 217–220, Aug. 2010.
[23]Y. G. Kim, K. Y. Shin, E. C. Lee, and K. R. Park, “Multimodal Biometric System Based on the Recognition of Face and Both Irises”. International Journal of Advanced Robotic Systems, vol. 9, pp. 1-6, 2012.
[24]M. Eskandari and Ö. Toygar, ”Person Identification Using Face and Iris Multimodal Biometric System”, Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), pp. 1-5, 2012.
[25]M. Eskandari and Ö. Toygar, ”Fusion of Face and Iris Biometrics Using Local and Global Feature Extraction Methods”, Signal, Image and Video Processing, vol. 8, no. 6, pp. 995–1006, 2014.
[26]M. Eskandari, Ö. Toygar, and H. Demirel, ”A New Approach for Face-Iris Multimodal Biometric Recognition Using Score Fusion”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 27, no. 3, May. 2013.
[27]H. F. Liau and D. Isa, “Feature Selection for Support Vector Machine Based Face-Iris Multimodal Biometric System”, Expert Systems with Applications, vol. 38, no. 9, pp. 11105–11111, Sep. 2011.
[28]M. Vasta, R. Singh, and A. Noore, “Integrating Image Quality in 2v-SVM Biometric Match Score Fusion”, International Journal of Neural Systems, vol. 17, no. 5, pp. 343–351, 2007.
[29]F. Wang and J. Han, “Multimodal Biometric Authentication Based on Score Level Fusion Using Support Vector Machine”, Opto-Electronics Review, vol. 17, no. 1, pp. 59–64, Mar. 2009.
[30]P. A. Johnson, F. Hua, and S. Schuckers, "Comparison of Quality-Based Fusion of Face and Iris Biometrics", International Joint Conference on Biometrics (IJCB), pp. 1-5, Oct. 2011.
[31]N. Wang, L. Lu, G. Gao, F. Wang, and S. Li, ”Multibiometrics Fusion Using Aczél-Alsina Triangular Norm”, KSII Transactions on Internet and Information Systems(TIIS), vol. 8, no. 7, pp. 2420-2433, Jul. 2014
[32]R. Connaughton, K. W. Bowyer, and P. J. Flynn, ”Fusion of Face and Iris Biometrics From a Stand-off Video Sensor”, Proceedings of the 22nd Midwest Artificial Intelligence and Cognitive Science Conference (MAICS 2011), pp. 99–106, Cincinatti, OH, Apr. 2011.
[33]R. Connaughton, K. W. Bowyer, and P. J. Flynn, “Fusion of Face and Iris Biometrics”, Handbook of Iris Recognition, pp. 219-237, Springer London, 2013.
[34]P. H. Lee, L. J. Chu, Y. P. Hung, S. W. Shih, C. S. Chen, and al., ”Cascading Multimodal Verification Using Face, Voice and Iris Information”, IEEE International Conference on Multimedia and Expo, pp. 847–850, Jul. 2007.
[35]A. Chikhaoui and A. Mokhtari, “Classification with Support Vector Machines: New Quadratic Programming Algorithm“, COSI’2013, Algiers, Algeria, Jun. 2013.
[36]SDUMLA-HMT Database: http://mla.sdu.edu.cn/sdumla-hmt.htm
[37]L. Masek, “Recognition of Human Iris Patterns for Biometric Identification”, PhD thesis, University of Western Australia, 2003.
[38]J. Daugman, “High Confidence Recognition of Persons by Rapid Video Analysis of Iris Texture”, European Convention on Security and Detection, pp. 244–251, May. 1995.
[39]A. Jain, K. Nandakumar and A. Ross, “Score Normalization in Multimodal Biometric Systems”, Pattern Recognition, vol. 38, no. 12, pp. 2270–2285, Dec. 2005.
[40]R. M. Bolle, J. H. Connell and S. Pankanti,” Guide to Biometrics”, Springer-Verlag, New York, 2004.
[41]A. Chikhaoui, B. Djebbar, and R. Mekki, ”New Method for Finding an Optimal Solution to Quadratic Programming Problem”, Journal of Applied Sciences, vol. 10, no. 15, pp. 1627-1631, 2010.
[42]Y. Yin, L. Liu, and X. Sun, ”SDUMLA-HMT: A Multimodal Biometric Database”, In Chinese Conference on Biometric Recognition (CCBR 2011), Springer Berlin Heidelberg, pp. 260–268, Dec. 2011.