Open AccessFUSION DECISION FOR A BIMODAL BIOMETRIC VERIFICATION SYSTEM USING SUPPORT VECTOR MACHINE AND ITS VARIATIONS
Author(s) -
Andrew Beng Jin Teoh,
Salina Abdul Samad,
Aini Hussain
Publication year - 2017
Publication title -
asean journal on science and technology for development/asean journal on science and technology for development
Language(s) - English
Resource type - Journals
eISSN - 2224-9028
pISSN - 0217-5460
DOI - 10.29037/ajstd.326
Subject(s) - support vector machine , biometrics , computer science , fusion , transformation (genetics) , artificial intelligence , pattern recognition (psychology) , expert system , machine learning , face (sociological concept) , decision support system , data mining , speech recognition , social science , biochemistry , chemistry , sociology , gene , philosophy , linguistics
This paw presents fusion detection technique comparisons based on support vector machine and its variations for a bimodal biometric verification system that makes use of face images and speech utterances. The system is essentially constructed by a face expert, a speech expert and a fusion decision module. Each individual expert has been optimized to operate in automatic mode and designed for security access application. Fusion decision schemes considered are linear, weighted Support Vector Machine (SVM) and linear SVM with quadratic transformation. The conditions tested include the balanced and unbalanced conditions between the two experts in order to obtain the optimum fusion module from these techniques best suited to the target application.