Manifold Learning of Overcomplete Feature Spaces in a Multimodal Biometric Recognition System of Iris and Palmprint

摘要

This paper presents a bimodal biometric recognition system based on iris and palmprint. Different wavelet-based filters including log Gabor, Discrete Cosine Transform (DCT), Walsh and Haar are used to extract features from images. Then we fuse iris and palmprint at the feature level by concatenating the feature vectors from two modalities. Since wavelet transforms generate huge number of features, a dimensionality reduction step is necessary to make the classification and matching steps tractable and computationally feasible. In this paper, two well-known dimensionality reduction algorithms including Laplacian eigenmaps and Singular Value Decomposition (SVD) are used to reduce the size of feature space. Applying these dimensionality reduction methods not only decreases the computational cost of matching remarkably but also it improves the accuracy of recognition by reducing the unnecessary model complexity. Eventually multiple classification techniques are used in the transformed feature spaces for the final matching and recognition. CASIA datasets for iris and palmprint are used in this study. The experiments show the effectiveness of our feature level fusion method and also the dimensionality reduction methods we used. Based on our experiments, our multimodal biometric system always outperforms the unimodal recognition systems with higher accuracy. Moreover, an appropriate dimensionality reduction algorithm always helps to improve the accuracy of classifier. Finally, the log Gabor filter extracts the most discriminative features from images compared to other wavelet transforms.