Multimodal biometric identification system with deep learning based feature level fusion using maximum orthogonal method

Abstract

Multimodal Biometrics are used to developed the robust system for Identification. Biometric such as face, fingerprint and palm vein are used for security purposes. In this Proposed System, Convolutional neural network is used for recognizing the image features. Convolutional neural networks are complex feed forward neural networks used for image classification and recognition due to its high accuracy rate. Convolutional neural network extracts the features of face, fingerprint and palm vein. Feature level fusion is done at Rectified linear unit layer. Maximum orthogonal component method is used for Fusion. In Maximum orthogonal component method, prominent features of biometrics are considered and fused together. This method helps to improve the recognition rates. Database are self-generated using these biometrics. Training and Testing is done using 4500 images of face, fingerprint and palm vein. Performance parameters are improved by this technique. The experimental results are better than conventional methods.