Fusion of face and iris biometrics using local and global feature extraction methods

Fusion of multiple biometrics combines the strengths of unimodal biometrics to achieve improved recognition accuracy. In this study, face and iris biometrics are used to obtain a robust recognition system by using several feature extractors, score normalization and fusion techniques. Global and local feature extractors are used to extract face and iris features separately, and then, the fusion of these modalities is performed on different subsets of face and iris image databases of ORL, FERET, CASIA and UBIRIS. The proposed method uses Local Binary Patterns local feature extractor and subspace Linear Discriminant Analysis global feature extractor on face and iris images, respectively. Face and iris scores are normalized using tanh normalization, and then, Weighted Sum Rule is applied for the fusion of these two modalities. Improved recognition accuracies are achieved compared to the individual systems and multimodal systems using other local or global feature extractors for both modalities.

[1]  John Law,et al.  Robust Statistics—The Approach Based on Influence Functions , 1986 .

[2]  Yulian Zhu,et al.  Subpattern-based principle component analysis , 2004, Pattern Recognit..

[3]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Ramachandra Raghavendra,et al.  Designing efficient fusion schemes for multimodal biometric systems using face and palmprint , 2011, Pattern Recognit..

[5]  Arun Ross,et al.  Handbook of Multibiometrics , 2006, The Kluwer international series on biometrics.

[6]  Arun Ross,et al.  Learning user-specific parameters in a multibiometric system , 2002, Proceedings. International Conference on Image Processing.

[7]  Ching-Han Chen,et al.  Fusion of Face and Iris Features for Multimodal Biometrics , 2006, ICB.

[8]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Anil K. Jain,et al.  Likelihood Ratio-Based Biometric Score Fusion , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Tieniu Tan,et al.  Combining Face and Iris Biometrics for Identity Verification , 2003, AVBPA.

[11]  Climent Nadeu,et al.  On Real-Time Mean-and-Variance Normalization of Speech Recognition Features , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[12]  Richa Singh,et al.  Integrating Image Quality in 2nu-SVM Biometric Match Score Fusion , 2007, Int. J. Neural Syst..

[13]  Dino Isa,et al.  Feature selection for support vector machine-based face-iris multimodal biometric system , 2011, Expert Syst. Appl..

[14]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Sasidhar Kothuru.  Automated Authentication using Information Fusion and Score Normalization in Multimodal Biometric Systems , 2011 .

[16]  Loris Nanni,et al.  Over-complete feature generation and feature selection for biometry , 2008, Expert Syst. Appl..

[17]  Xiaogang Wang,et al.  Random Sampling for Subspace Face Recognition , 2006, International Journal of Computer Vision.

[18]  Vijayan K. Asari,et al.  An improved face recognition technique based on modular PCA approach , 2004, Pattern Recognit. Lett..

[19]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[20]  Jiuqiang Han,et al.  Multimodal biometric authentication based on score level fusion using support vector machine , 2009 .

[21]  Patrick J. Flynn,et al.  Image understanding for iris biometrics: A survey , 2008, Comput. Vis. Image Underst..

[22]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..

[23]  B. V. K. Vijaya Kumar,et al.  Graphical Model Approach to Iris Matching Under Deformation and Occlusion , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Babak Nadjar Araabi,et al.  Iris Recognition for Partially Occluded Images: Methodology and Sensitivity Analysis , 2007, EURASIP J. Adv. Signal Process..

[25]  Florence Rossant,et al.  Reliable Eyelid Localization for Iris Recognition , 2008, ACIVS.

[26]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[27]  Libor Masek,et al.  MATLAB Source Code for a Biometric Identification System Based on Iris Patterns , 2003 .

[28]  Hakan Altinçay,et al.  Preserving spatial information and overcoming variations in appearance for face recognition , 2011, Pattern Analysis and Applications.