Palmprint Recognition with Statistical, Wavelet and Local Feature Extraction Methods

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 transformatio