Palmprint recognition with Local Micro-structure Tetra Pattern

Human palmprint-based biometric solutions have been studied extensively in both controlled and uncontrolled environments. However, the majority of existing methods do not reliably handle variations of translation, rotation, and blurriness of one's palm within the range of acceptable tolerance, which largely degrades the performance. Therefore, this paper presents a unique local descriptor called Local Micro-structure Tetra Pattern (LMTrP) and its application to palmprint recognition. The proposed descriptor takes advantage of local descriptors’ direction as well as thickness. In this paper, the palmprint image is first filtered with the line-shaped filter to effectively eliminate unnecessary features. Then, local region histograms of LMTrP are extracted and concatenated into one feature vector to represent the given image. Finally, the kernel linear discriminant analysis is applied on the feature vector for dimension reduction. The experimental results indicate that the proposed methods significantly outperform the state-of-the-art methods without the need to align the palmprint images.