Fusion of multimodal biometric authentication using gradient pyramid, PCA and DWT

Authentication and identification is the most challenging task in our daily life. Biometric system provides an automatic identification of an individual using his/her behavioural or physiological traits. In this work,
multimodal biometric traits namely fingerprint and iris, have been used. These traits were pre-processed using Wiener filter and applying some morphological operations. The pre-processed biometric traits were segmented and fused using
three algorithms namely discrete wavelet transform (DWT), principal component analysis (PCA) and gradient pyramid (GP). The fused biometric traits using GP provides a better result without losing the meaningful information. The feature extraction and classification were carried out using
grey scale co-occurrence matrices (GLCM) and support vector machine (SVM). Authentication using fused biometric traits gives accuracy as 83.75, whereas the accuracy using fingerprint 73.75% and iris was 78.48%.