Title: Design of an ensemble face recognition system using optimal local features for unconstraint and age difference environment

Authors: Dipak Kumar; Ravi Kant Kumar; Jogendra Garain; Dakshina Ranjan Kisku; Jamuna Kanta Sing; Phalguni Gupta

Addresses: National Institute of Technology Durgapur, West Bengal, India ' Department of Computer Science and Engineering, SRM University AP, India ' Raghunathpur Government Polytechnic College, West Bengal, India ' National Institute of Technology, Durgapur, India ' Department of Computer Science and Engineering, Jadavpur University, India ' GLA University, Mathura, India

Abstract: Biometric authentication especially using face recognition, is still a big, challenging problem. The face exhibits various semantic information with its numerous expressions. The exhibitions of dynamic expressions of faces are the main challenges for the biometric system. However, researchers are continuously trying to enhance facial recognition robustness. This work proposes a multi-classifier-based ensemble system for effective face recognition. Our proposed system has two modules: 1) an optimisation module that decreases computational cost using feature sets; 2) a fusion module with multiple classifiers to advance accuracy. The feature set is extracted using local descriptors named LBP, DS-LBP, and LGS. Feature sets are optimised using a genetic algorithm (GA). Optimised feature sets are classified distinctly, and results are united using decision-level fusion methods like AND-rule, OR-rule, and majority voting. Experiments accomplished on LFW, BioID, and LAG datasets and it shows that the proposed ensemble system is more efficient and robust.

Keywords: face recognition system; local binary pattern; LBP; local graph structure; SLGS; densely sampled local binary pattern; DS-LBP; ensemble system; genetic algorithms; majority voting.

DOI: 10.1504/IJBM.2026.154562

International Journal of Biometrics, 2026 Vol.18 No.4, pp.297 - 330

Received: 07 Jan 2024
Accepted: 07 Jul 2024
Published online: 06 Jul 2026 *

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