Palmprint recognition using Kekre's wavelet's energy entropy based feature vector

Palmprints are one of the oldest biometric traits used by mankind. It is highly universal and moderate user co-operation is required in implemented system. Palmprints are rich in texture information which can be used classification purpose. Wavelets are very good in extracting localized texture information. In this paper a new and faster type of wavelets called kekre's wavelets are used for extracting feature vector from palmprints. Multilevel decomposition is performed and feature vectors are matched using Euclidian distance and Relative Energy Entropy. The results indicate that kekre's wavelets are viable option for extracting texture information from palmprints and provide good accuracy with faster performance.