Improvement in palmprint recognition rate using fusion of multispectral palmprint images

Palmprint recognition using Biometrics is one of the emerging technologies, which recognizes a person based on the principle lines, wrinkles and ridges on the surface of the palm. These line structures are stable and remain unchanged throughout the life of an individual. More importantly, no two palm prints from different individuals are the same, and normally people do not feel uneasy to have their palmprint images taken for testing. Therefore palmprint recognition offers a promising future for medium-security access control systems. The main purpose of this project is to enhance the reliability and accuracy in person identification. Person identification is done by extracting the features of multispectral palmprint images in region of interest after preprocessing of multispectral palmprint images. There are many filters used for feature extraction such as stack filter, curvelet filer etc., Here 2D-gabor filter is used since it is an effective tool for texture analysis and more robust to brightness. The extracted feature is fused at score level and image level. Finally, the Euclidean distance is used for matching of palmprint features in the database. The simulated results provide high accuracy while using the fusion of multispectral palmprint images.