Statistical analysis of various kernel parameters on SVM based multimodal fusion

• Main Authors: Vora, Aarohi, Paunwala, Chirag N., Paunwala, Mita
• Format: Conference Proceeding
• Language: English
• Subjects: Biometrics; Face; Kernel; kernel methods; Match score level fusion; Mathematical model; Multimodal system; Polynomials; Support Vector Machine (SVM) fusion system; Support vector machines; Training
• ISSN: 2325-940X
• DOI: 10.1109/INDICON.2014.7030414

Biometric systems accurately recognise/authenticate an individual to access his confidential data/accounts. When multiple traits are fused together at feature/ score/ decision level, it results into highly accurate multimodal systems. This system improvise rate of recognizing an individual. Multiple biometric traits cannot be cloned simultaneously and hence it is highly secured system. The match scores of different persons are sufficient enough to recognise them and differentiate them from each other. The match scores do not require higher storage capacity as well as higher computational complexity. Hence, match score fusion is highly preferable to recognize an individual. Fusion at match score level has been carried out by several researchers with various state of arts namely weighted sum rule, product rule, majority voting rule, Support vector machine (SVM), Bayesian fusion, fuzzy rule method, etc. In this paper SVM based fusion of match scores for face and fingerprint biometric trait is implemented. Main research focus of this paper is on statistical analysis of different kernel methods namely Polynomial kernel, Radial Basis Function (RBF) kernel and Multilayer perceptron (MLP) kernel used for training SVM. The statistical analysis is based on training time required for training SVM using all three kernel methods as well upon the performance curve in terms of recognition rates i.e. Genuine Acceptance Rate (GAR) and False Acceptance Rate (FAR) of SVM fused system. SVM fusion has been implemented in MATLAB software and the results reveal that RBF kernel based SVM fused system requires the lowest training time as compared to other kernel methods. Even the recognition performance of RBF based SVM system is higher as compared to other kernel based systems i.e. GAR of RBF fused system increases and is better as compared to other kernel based SVM systems.