Authors : Ojo Omotayo Job; Adetunji Abigail Bola; Alade Oluwaseun Modupe; Jeremiah Yetomiwa Sinat; Adedayo-Ajayi Victoria Oluwaseyi

Volume/Issue : Volume 11 - 2026, Issue 10 - October

Google Scholar : https://tinyurl.com/2s4jxyc9

DOI : https://doi.org/10.38124/ijisrt/26Oct020

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Differences in feature dimensionality, quality, and reliability can cause face and fingerprint recognition methods to contribute unequally in a bimodal biometric classification system. This study proposes a Tent-map-enhanced Chaotic Grey Wolf Optimizer (ECGWO) for feature selection in a combined face–fingerprint biometric authentication system. Rather than using the random vectors in the standard Grey Wolf Optimizer (GWO) position-update process, the proposed approach uses two separate Tent-map chaotic sequences and applies chaotic modulation to balance exploration and exploitation. The algorithm extracts Local Binary Pattern (LBP) descriptors from the preprocessed face and fingerprint images, then normalizes and combines them.

Keywords : Grey Wolf Optimizer, Tent Map; Feature Selection, Bimodal Biometrics, Face Recognition, Fingerprint Recognition, Support Vector Machine.

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