Contactless Palmprint Recognition Using Binarized Statistical Image Features-Based Multiresolution Analysis

Abstract

In recent years, palmprint recognition has gained increased interest and has been a focus of significant research as a trustworthy personal identification method. The performance of any palmprint recognition system mainly depends on the effectiveness of the utilized feature extraction approach. In this paper, we propose a three-step approach to address the challenging problem of contactless palmprint recognition: (1) a pre-processing, based on median filtering and contrast limited adaptive histogram equalization (CLAHE), is used to remove potential noise and equalize the images’ lighting; (2) a multiresolution analysis is applied to extract binarized statistical image features (BSIF) at several discrete wavelet transform (DWT) resolutions; (3) a classification stage is performed to categorize the extracted features into the corresponding class using a K-nearest neighbors (K-NN)-based classifier. The feature extraction strategy is the main contribution of this work; we used the multiresolution analysis to extract the pertinent information from several image resolutions as an alternative to the classical method based on multi-patch decomposition. The proposed approach was thoroughly assessed using two contactless palmprint databases: the Indian Institute of Technology—Delhi (IITD) and the Chinese Academy of Sciences Institute of Automatisation (CASIA). The results are impressive compared to the current state-of-the-art methods: the Rank-1 recognition rates are 98.77% and 98.10% for the IITD and CASIA databases, respectively.

Enhanced histogram of oriented lines for palmprint recognition

“…For palmprint recognition, researchers have proposed many methods that can be classified into different types, such as texture-based [16,17] , subspace learning-based [18][19][20] , orientation coding-based [21][22][23] , deep learning-based [24][25][26][27] methods.…”

“…Texture-based methods, often used for some recognition tasks in biometrics, are also very effective for palmprint recognition. Amrouni et al [16] introduced a feature extraction approach called multiresolution analysis. Zhang et al [17] proposed a contactless palmprint recognition technique integrating HMS-CLBPs for scale-invariant texture encoding with WSRC for pattern matching.…”

Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

“…Palmprint image recognition is then performed by computing the reconstruction residuals between the test sample and its synthesized approximation under each classspecific sub-dictionary. Amrouni et al (2022) [78] introduced a feature extraction approach called multiresolution analysis. First, they applied the discrete wavelet transform (DWT) to an original palmprint image.…”

Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph

“…Therefore, the feature encoding for handprints needs to be designed to incorporate the automated image alignment (for geometrical transformations) and less computational cost. Further, the feature representation of contactless palm print images cannot be directly used in contact images due to low image quality and low resolution because contactless palm print or handprint approaches use multiview image [42,43] and multiresolution [44] image capture with multimodal approaches [45,46]. These strategies help performance-related issues arising from image quality and low resolution.…”