Feature level fusion framework for multimodal biometric system based on CCA with SVM classifier and cosine similarity measure

The multimodal biometric system using feature level fusion offers more accurate and reliable recognition performance than the unimodal system. But in practice, feature level fusion is challenging to perform when biometric modalities have heterogeneous and incompatible feature representation and enforce the final decision more confidently. One of the main concerns in the fusion of features is to drive the highly discriminatory representation amongst different biometric modalities. This paper aims to design a framework for an efficient feature level fusion based on canonical correlation analysis (CCA) with a support vector machine (SVM) classifier to get a highly discriminant and affine invariant fused feature vector. The principal component analysis (PCA) + CCA subspace approach is used to achieve dimensionality reduction and feature fusion in a coherent manner, This approach eliminates the need for a complex matcher/classifier design to process a fused feature vector and also reduces computational complexity. The experimental findings for the SDUMLA-HMT multimodal database demonstrate that CCA on the extracted feature sets of iris and fingerprint modalities results in reasonably better multimodal classification accuracy with a substantial reduction in the feature dimensions. Using SVM, we achieved a classification accuracy of 100%. In this paper furthermore, three different distance measures are explored to test the efficacy of the proposed CCA-based feature level fusion approach. The best recognition performance is achieved in terms of an equal error rate (EER) of 0.176% for the cosine similarity measure. We also compared the proposed approach with the match score level fusion method. The proposed feature level fusion approach excels the recognition performance in contrast to the other literature approaches.