A comparison of gaussian mixture and artificial neural network models for voiced-based access control system of building security

Abstract

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. However, these conventional devices can be stolen, forgotten, lost, guessed or impersonated with accuracy. Biometric system based on behavioral and/or physiological characteristics of person becomes popular as an alternative method to overcome the problem of conventional method. In this paper, voice-based biometric system is introduced for access control of building security. 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 individual's voice cannot be stolen, lost, forgotten, guessed, or impersonated with accuracy. In the proposed system, the access may be authorized simply by means of an enrolled user speaking into a microphone attached to the system. The proposed system then will decide whether to accept or reject the user's identity claim or possibly to report insufficient confidence and request additional input be/ore making the decision. Two approaches are adopted and evaluated to model the authorized persons, namely classical Gaussian Mixture Model (GMM) and artificial neural network (ANN). Experimental result confirms that in terms of acceptance rate (FAR) and false rejection rate (FAR), the proposed voice-based access control with ANN model is better than that with GMM.