Normalization and Weighting Techniques Based on Genuine-Impostor Score Fusion in Multi-Biometric Systems

The performance of a multi-biometric system can be improved using an efficient normalization technique under the simple sum-rule-based score-level fusion. It can also be further improved using normalization techniques along with a weighting method under the weighted sum-rule-based score-level fusion. In this paper, at first, we present two anchored score normalization techniques based on the genuine and impostor scores. Specifically, the proposed normalization techniques utilize the information of the overlap region between the genuine and impostor scores and their neighbors. Second, we propose a weighting technique that is based on the confidence of the matching scores by considering the mean-to-maximum of genuine scores and mean-to-minimum of impostor scores. A multi-biometric system having three biometric traits, fingerprint, palmprint, and earprint, is utilized to evaluate the performance of the proposed techniques. The performance of the multi-biometric system is evaluated in terms of the equal error rate and genuine acceptance rate @0.5% false acceptance rate. The receiver operating characteristics are also plotted in terms of the genuine acceptance rate as a function of the false acceptance rate.

[1]  Sridha Sridharan,et al.  Score-Level Multibiometric Fusion Based on Dempster–Shafer Theory Incorporating Uncertainty Factors , 2015, IEEE Transactions on Human-Machine Systems.

[2]  Ajay Kumar,et al.  Incorporating Cohort Information for Reliable Palmprint Authentication , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

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

[4]  Junbin Gao,et al.  Fingerprint Matching using A Hybrid Shape and Orientation Descriptor , 2011 .

[5]  Ioannis A. Kakadiaris,et al.  Feature fusion for facial landmark detection , 2014, Pattern Recognit..

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

[7]  Pingzhi Fan,et al.  Performance evaluation of score level fusion in multimodal biometric systems , 2010, Pattern Recognit..

[8]  Ralph Gross,et al.  Robust Biometric Person Identification Using Automatic Classifier Fusion of Speech, Mouth, and Face Experts , 2007, IEEE Transactions on Multimedia.

[9]  S. Shekhar,et al.  Personal Identification Using Multibiometrics Rank-Level Fusion , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[10]  Samy Bengio,et al.  A Study of the Effects of Score Normalisation Prior to Fusion in Biometric Authentication Tasks , 2004 .

[11]  Marina L. Gavrilova,et al.  Multimodal Biometric System Using Rank-Level Fusion Approach , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  David Zhang,et al.  3-D Palmprint Recognition With Joint Line and Orientation Features , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[13]  Stefano Berretti,et al.  Boosting 3D LBP-based face recognition by fusing shape and texture descriptors on the mesh , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

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

[15]  Fella Hachouf,et al.  Score-Level Fusion of Face and Voice Using Particle Swarm Optimization and Belief Functions , 2015, IEEE Transactions on Human-Machine Systems.

[16]  Anil K. Jain,et al.  Multibiometric Cryptosystems Based on Feature-Level Fusion , 2012, IEEE Transactions on Information Forensics and Security.

[17]  Fei Zhou,et al.  Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion , 2014, Inf. Sci..

[18]  Mohamed Abdel-Mottaleb,et al.  Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition , 2016, IEEE Transactions on Information Forensics and Security.

[19]  Roberto Togneri,et al.  An efficient reliability estimation technique for audio-visual person identification , 2013, 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA).

[20]  M. Omair Ahmad,et al.  A new anchored normalization technique for score-level fusion in multimodal biometrie systems , 2016, 2016 IEEE International Symposium on Circuits and Systems (ISCAS).

[21]  David Zhang,et al.  Face and palmprint pixel level fusion and Kernel DCV-RBF classifier for small sample biometric recognition , 2007, Pattern Recognit..

[22]  Anil K. Jain,et al.  Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Ryuei Nishii.  A Markov random field-based approach to decision-level fusion for remote sensing image classification , 2003, IEEE Trans. Geosci. Remote. Sens..

[24]  Juan Miguel García-Gómez,et al.  Fusing actigraphy signals for outpatient monitoring , 2015, Inf. Fusion.

[25]  Colin Haynes,et al.  Enhanced damage localization for complex structures through statistical modeling and sensor fusion , 2015 .

[26]  Madasu Hanmandlu,et al.  Score level fusion of multimodal biometrics using triangular norms , 2011, Pattern Recognit. Lett..

[27]  Martin Heckmann,et al.  Noise Adaptive Stream Weighting in Audio-Visual Speech Recognition , 2002, EURASIP J. Adv. Signal Process..

[28]  Phalguni Gupta,et al.  TARC: A novel score fusion scheme for multimodal biometric systems , 2014, 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM).

[29]  Mohammed Bennamoun,et al.  A confidence-based late fusion framework for audio-visual biometric identification , 2015, Pattern Recognit. Lett..

[30]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Building binary-tree-based multiclass classifiers using separability measures , 2010, Neurocomputing.

[31]  Michael Wagner,et al.  Robust face-voice based speaker identity verification using multilevel fusion , 2008, Image Vis. Comput..

[32]  M. Omair Ahmad,et al.  A novel normalization technique for multimodal biometric systems , 2015, 2015 IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS).

[33]  Naser Damer,et al.  Performance Anchored Score Normalization for Multi-biometric Fusion , 2013, ISVC.

[34]  Andrew Beng Jin Teoh,et al.  Integrated biometrics template protection technique based on fingerprint and palmprint feature-level fusion , 2014, Inf. Fusion.

[35]  Mohammed Bennamoun,et al.  An efficient 3D face recognition approach based on the fusion of novel local low-level features , 2013, Pattern Recognit..

[36]  Himanshu S. Bhatt,et al.  On Recognizing Faces in Videos Using Clustering-Based Re-Ranking and Fusion , 2014, IEEE Transactions on Information Forensics and Security.

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

[38]  Marina L. Gavrilova,et al.  Decision Fusion for Multimodal Biometrics Using Social Network Analysis , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[39]  Mohamed Abdel-Mottaleb,et al.  An Efficient 3-D Ear Recognition System Employing Local and Holistic Features , 2012, IEEE Transactions on Information Forensics and Security.

[40]  M. Omair Ahmad,et al.  Score reliability based weighting technique for score-level fusion in multi-biometric systems , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[41]  Phalguni Gupta,et al.  Quantitative Evaluation of Normalization Techniques of Matching Scores in Multimodal Biometric Systems , 2007, ICB.

[42]  Raymond N. J. Veldhuis,et al.  Threshold-optimized decision-level fusion and its application to biometrics , 2009, Pattern Recognit..

[43]  Xiao-Yuan Jing,et al.  Face and palmprint feature level fusion for single sample biometrics recognition , 2007, Neurocomputing.