Palmprint Recognition with Statistical, Wavelet and Local Feature Extraction Methods

ABSTRACT: Palmprint recognition has gained significant importance in biometric and multi- biometric identification systems and it has been widely used in most of the security projects. The reason behind this is that a palmprint is a unique sample for each individual person. It is a biometric signature of fix shape; a born baby holds the same shape up to death. Nowadays most of the studies focus on enhancing the recognition rate and determining the age and gender of palmprint images. In this thesis, three different feature extraction techniques have been applied on images of a well know palmprint database. The three methods can be characterized as a statistical method namely principle component analysis (PCA), a transformation method namely Haar wavelets and a texture method namely local binary pattern (LBP). The aim of applying different feature extraction methods is to compare their relative performance and determine the best method for palmprint recognition. Moreover, hybrid methods combining the algorithms mentioned above have been created in order to take the advantage of two or more feature extraction methods. Outputs individual method are fused using voting techniques. Keywords: Palmprint recognition, Principal Component Analysis, Local Binary Patterns, Haar Wavelets. …………………………………………………………………………………………………………………………