Publications Scientifiques

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Author: search.filters.author.Adjabi, Insaf

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    Contactless palmprint recognition using binarized statistical image Features-Based multiresolution analysis
    (MDPI, 2022) Amrouni, Nadia; Benzaoui, Amir; Bouaouina, Rafik; Khaldi, Yacine; Adjabi, Insaf; Bouglimina, Ouahiba
    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
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    Identity recognition based on palmprints : the preliminary results
    (IEEE, 2022) Amrouni, Nadia; Benzaoui, Amir; Adjabi, Insaf
    Private and automatic recognition in many applications, such as forensic, access control, and surveillance systems, has become necessary in recent years. Biometrics, which treats individuals' identification based on physical or behavioral characteristics, has emerged as an effective automated identification technology, offering more properties and advantages than conventional protection. The use of palmprints in biometric authentication has dramatically increased and has been used extensively in management systems for businesses, Internet of Thinks, and individuals. In this field, the palmprint is considered a new modality, a unique entity that is stable over time and has a rich information structure. As part of this work, the local binary pattern descriptor (LBP) was used and tested under several configurations to extract the palmprint modality's optimal and efficient characteristics. As preliminary results, our experiments on the IITD Palmprint V1 database exhibit impressive performance