Studying Of Multimodal Biometric System Using Different Svm Kernels (Trinity Journal of Management, IT & Media (TJMITM), 2013)
Biometric security system is based on a binary classification problem: reject or accept identity claim. The  performance and robustness of biometric security systems  can be improved by combining two or more different  modalities (speech, face, fingerprint, etc.). In literature,  one of the issues is to improve the performance of  multimodal biometric system. In this paper, we investigated the performance of multimodal biometric  system using different SVM kernel functions and its  parameters. Our experiment on XM2VTS dataset shows systems have been developed based on different  biometrics characteristics (fingerprint, face, speech, iris  etc.) which help to distinguish individuals from each other.  Each biometric has its own advantages and drawbacks  due to its discriminative power, complexity, robustness  involved. Research in last few years has shown that no  single modal biometric system can achieve 100%  authentication accuracy. This problem can be alleviated by  combining two or more biometric modalities [1], also  known as the field of multimodal biometric authentication.  A user verification scenario involving two modalities (Face  and Speech) [2] is figured out in Fig. 1. that the performance of multimodal biometric system is  improved with the proper choice of kernel function and its  parameters.
Keywords: Binary classifiers; support vector machine; biometric system; feature extraction; fusion classifier