Multimodal Person Authentication Using Qualitative SVM With Fingerprint, Teeth and Voice Modalities
Multimodal biometrics systems are becoming increasingly efficient over the unimodal system, especially for the securing handheld devices. However, the challenge with this authentication system is the relative degradation of the biometric modalities involved in the development and test data respectively. To overcome this problem, in this paper the authors propose a novel Qualitative Support Vector Machine (SVM) classifier with Face, teeth, and fingerprint as biometric traits. The test scores of individual modalities are adjusted according to their relative quality and then passed to binary SVM classifier. The experiments were conducted over a database collected from 20 individuals with three instances of all the three traits.