An Efficient Algorithm for Palmprint Based Personal Authentication System with Rotation Invariant Feature Vectors

This paper presents a high performance palmprint personal authentication system implementation method with number of experimental result. A major approach for palmprint recognition today is to extract feature vectors corresponding to individual palmprint images and to perform palmprint matching based on some distance metrics. One of the difficult problems in feature-based recognition is that the matching performance is significantly influenced by many parameters in feature extraction process, which may vary depending on environmental factors of image acquisition. The Zernike moment feature extraction plays the main role in the proposed system for different orientation hand image ROI identification. Unsharp filtered palmprint images makes possible to achieve highly robust palmprint recognition. MATLAB 7.9 is used for the system verification. Experimental evaluation using a palmprint image database clearly demonstrates an efficient matching performance of the proposed system.