Efficient palmprint biometric identification systems using deep learning and feature selection methods

Autor: Selma Trabelsi, Djamel Samai, Fadi Dornaika, Azeddine Benlamoudi, Khaled Bensid, Abdelmalik Taleb-Ahmed
Přispěvatelé: Université Kasdi Merbah Ouargla, Universitat Autònoma de Barcelona (UAB), Ikerbasque - Basque Foundation for Science, University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/EHU), Center for Machine Vision Research (CMV), University of Oulu, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), COMmunications NUMériques - IEMN (COMNUM - IEMN), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), The authors gratefully acknowledge the Directorate General for Scientific Research and Technological Development (DGRSDT) of Algeria for the financial support to this work.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Neural Computing and Applications
Neural Computing and Applications, 2022, 34, pp.12119-12141. ⟨10.1007/s00521-022-07098-4⟩
ISSN: 0941-0643
1433-3058
Popis: Over the past two decades, several studies have paid great attention to biometric palmprint recognition. Recently, most methods in literature adopted deep learning due to their high recognition accuracy and the capability to adapt with different acquisition palmprint images. However, high-dimensional data with a large number of uncorrelated and redundant features remain a challenge due to computational complexity issues. Feature selection is a process of selecting a subset of relevant features, which aims to decrease the dimensionality, reduce the running time, and improve the accuracy. In this paper, we propose efficient unimodal and multimodal biometric systems based on deep learning and feature selection. Our approach called simplified PalmNet–Gabor concentrates on the improvement of the PalmNet for fast recognition of multispectral and contactless palmprint images. Therefore, we used Log-Gabor filters in the preprocessing to increase the contrast of palmprint features. Then, we reduced the number of features using feature selection and dimensionality reduction procedures. For the multimodal system, we fused modalities at the matching score level to improve system performance. The proposed method effectively improves the accuracy of the PalmNet and reduces the number of features as well the computational time. We validated the proposed method on four public palmprint databases, two multispectral databases, CASIA and PolyU, and two contactless databases, Tongji and PolyU 2D/3D. Experiments show that our approach achieves a high recognition rate while using a substantially lower number of features.
Databáze: OpenAIRE
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