Abstract
This paper presents a new method to recognize the person's identity through their palmprints. Palmprint recognition is among the most reliable physiological characteristics that can be used especially in forensic applications thanks to its simplicity and its ease of use, its user friendliness and high identification reliability. Accordingly, it has gained great popularity within the pattern recognition field over the past three decades. In this paper, we suggest a new approach for personal identification based on palmprint features extracted using the various methods of fractal theory. These methods have been broadly applied in image processing fields to estimate the fractal dimensions of an image as an important parameter for the analysis of objects of irregular shapes of the texture image. The novelty of this approach is two-fold. On the one hand, we apply the Box counting (BC), the Mass Radius (MS) and the Cumulative Intersection (CumInt) methods to extract the palmprint texture information. On the other hand, the combination of efficient information from the three descriptors has been presented in order to make identification system more efficient and achieve better performances. Then, we explore such texture information features by using classical machine learning techniques: the K-Nearest Neighbor (KNN), the Support Vector Machine (SVM) and the Multiclass Random Forest classification algorithms. The results of the experiments conducted on two large datasets show that our proposed method gives better recognition rates of about 96.35% for CASIA-Palmprint dataset and 95.98% for IITD-Touchless- Palmprint dataset. These results obtained are compared to other well-known state-of-the-art approaches.