Design of Fusion Classifiers for Voice-Based Access Control System of Building Security

Secure buildings are currently protected from unauthorized access by a variety of devices. Nowadays, there are many kinds of devices to guarantee the building security such as PIN pads, keys both conventional and electronic, identity cards, cryptographic and dual control procedures. In this paper, voice-based biometric system is introduced for access control. The ability to verify the identity of a person by analyzing his/her speech, or speaker verification, is an attractive and relatively unobtrusive means of providing security for admission into an important or secured place. An individualpsilas voice cannot be stolen, lost, forgotten, guessed, or impersonated with accuracy. In the field of speaker verification, the main objective is to achieve the highest possible classification accuracy. The proposed system focused on combining the classification scores. In score fusion, each feature set is modeled separately, and the output score of the classifiers are combined to give the overall match score. Furthermore, for each classifier score, an a priori weight is set based on the level of confidence of the feature set and the classifier. The classifiers involved in this work are Gaussian mixture models (GMMs), multilayer feedforward network (MFN) and support vector machines (SVMs). Experimental result confirms that in terms of false acceptance rate (FAR) and false rejection rate (FRR), the fusion classifiers is effective to use in the proposed system.