Biometric Authentication Using Fused Multimodal Biometric

Biometrics are basically based on the expansion of pattern recognition systems. At present, electronic or optical sensors like cameras and scanning devices are used to capture images, recordings or measurements of a person's ‘unique’ characteristics. These technologies are being utilized across a range of applications like security, prevention of cyber crime and border control, public aid/social benefits, customs, immigration, passport and healthcare identity verification, as well as commercial enterprises use. Most biometric systems that are typically use a single biometric trait to establish identity have some challenges like Noise in sensed data which increases False Acceptance Rate (FAR) of the system, Non-universality which reduces Genuine Acceptance Rate (GAR). Hence the security afforded by the biometric system mitigates its benefits. In this paper, we propose a Fused Multimodal systems which also have several advantages over unibiometric systems such as, enhanced verification accuracy, larger feature space to accommodate more subjects and higher security against spoofing. The proposed enhanced multimodal authentication system is based on feature extraction(using fingerprint, retina and fingervein) and key generation (using RSA). The experimental evaluation implemented using MATLAB 2014, illustrates the significance improvement in the performance of multimodal biometrics with RSA have GAR of 95.3% and FAR of 0.01%.