Efficient Multimodal Biometric System Based on Feature Level Fusion of Palmprint and Finger Vein


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Abstract


Biometric authentication is playing a vital role in providing security and privacy. This paper presents a contemporary approach for identifying an individual using the multimodal biometrics has great demands to overcome the issues involved in single trait system.  Finger vein and palmprint biometric is a promising technology, now-a-days widely used because of its important features such as resistant to criminal tampering, high accuracy, ease of feature extraction and fast authentication speed. The features extracted from the preprocessed finger vein and palmprint images using Contourlet Transform reduce the overall dimensionality and computational complexity. Fusion at feature level utilizes the fused feature vectors of finger vein and palmprint using the Discrete Stationary Wavelet Transform (DSWT) to improve the overall performance of the multimodal biometric system. Integrating the biometric traits increases the robustness of the person identification and reduces fraudulent access. Experimental results based on homologous database demonstrate that the proposed system is very efficient to reduce the False Rejection Rate (FRR) and False Acceptance Rate (FAR).
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Keywords


Multimodal Biometrics; Countourlet Transform; Feature Level Fusion; DSWT

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