Image Fusion using PCA in Multifeature Based Palmprint Recognition

Biometric technology offers an effective approach to identify personal identity by using individual’s unique, reliable and stable physical or behavioral characteristics. Palmprint is a unique and reliable biometric characteristic with high usability. The composite algorithm used estimates the orientation field of the palmprint from which multiple features is extracted. Fusion increases the system accuracy and robustness in person recognition. The first kind of fusion is multiple features from one palmprint image. The existing system uses this technique through multiple features like minutiae, density map orientation, and principal line map from each palmprint image. The proposed paper uses multi-image fusion. The PCA-based image fusion technique adopted here improve resolution of the images in which images to be fused are firstly decomposed into sub images with different frequency and then the information fusion is performed and finally these sub images are reconstructed into a result image with plentiful information. The PCA algorithm builds a fused image of several input images as a weighted superposition of all input images. The resulting image contains enhanced information as compared to individual images. This image is used for palmprint recognition. A database containing multiple images of the same palmprint is used. The task of palmprint matching is to calculate the degree of similarity between an input test image and a training image from database. A normalized Hamming distance method is adopted to determine the similarity measurement for palmprint matching. Index Terms — Density map, Hamming distance, Multiimage fusion, Minutiae, PCA, Principal line map.