Efficent Approach Use to Increase accuracy Multimodal Biometric System for Feature Level Fusion

Security system comprised of a single form of biometric information cannot fulfil user’s expectations and may suffer from noisy sensor data, intra and inter class variations and continuous spoof attacks. To overcome some of these problems, multimodal biometric aims at increasing the reliability of biometric systems through utilizing more than one biometric in decision-making process.In this paper we propose a Efficient and Robust Multimodal Biometric System for feature level fusion that combines the information to investigate whether the integration of fingerprints and signatures . Proposed system extracts Gabor texture from the preprocessed fingerprints and signatures sample. The feature vectors attained from different methods are in different sizes and the features from equivalent image may be correlated. Therefore, we proposed wavelet-based fusion techniques. Finally apply neural network’s Cascaded feed forward Back propagation Algorithm to Train Neurons for recognition.proposed approach is authenticated for their accuracy on Fingerprints virtual database fused with signature virtual database of 16 users. The experimental results demonstrated that the proposed multimodal biometric system achieves a recognition accuracy of 99.8% and with false rejection rate (FRR) of = 1