A serious issue in developing automatic palmprint verification systems is the accurate and robust palm image cropping and feature extraction in order to produce high recognition accuracy. Feature extraction such as local binary patterns (LBP) can be used to describe palm image texture characteristics since the palm print image has a rich number of texture features. However, using LBP only to represent a local feature is not enough information to categorize the palmprint image. Our paper proposes a combination of LBP with KAZE feature detection to extract the features exist in palm image after applying the LBP. KAZE feature detection is able to select the most discriminant features which produce better class separation in the feature space. KAZE is used to select the most dominant features exist in LBP image descriptor. The performance evaluation is performed using benchmark PolyU dataset. The best result of the experiment work using PolyU palmprint database is 98% recognition rates.
