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Multimodal biometric-information fusion using the Radon transform

Abstract. This paper proposes multimodal biometric-based personal identification. Palmprint and fingerprint modalities are utilized in the proposed model due to the availability of more distinctive features and the ease of capture. Principle lines are the main discriminative features in the palmprint, whereas orientations of the ridge and valley structures are the main features by which to identify the fingerprint. To extract these features, the use of the Radon transform is proposed in this work. However, the Radon transform is sensitive to the orientation. In order to make the model rotation invariant and insensitive to noise, a normalization process is generally used. Here, a logarithm-based normalization process has been utilized in the proposed model. A Euclidean-based matching process that is invariant to the rotation has been used. The proposed model is applicable to low resolution images and is invariant to rotation, insensitive to noise, and has less computational complexity as compared to other models.