Wavelet Transform-Based Feature Extraction for Face Kinship Verification

Belabbaci El Ouanas; Mohammed Khammari; Gharbi Akram Abderraouf; Berkani Mohamed Rafik Aymene; Chouchane Ammar

doi:10.20944/preprints202411.0264.v1

Preprint Communication Version 1 This version is not peer-reviewed

Version 1 : Received: 3 November 2024 / Approved: 4 November 2024 / Online: 5 November 2024 (11:01:43 CET)

Kinship verification using facial images has become an important area of research within computer vision. This paper presents a robust kinship verification system that leverages Histograms of a Two-Dimensional Discrete Wavelet Transform (Hist-2D-DWT) to improve recognition accuracy. The system extracts planar features by decomposing images into four coefficients—approximation, horizontal, vertical, and diagonal—via discrete wavelet transform. Comprehensive experiments conducted on the TSKinFace and Cornell KinFace datasets validate the method’s effectiveness, achieving accuracies of 93.46% and 89.61%, respectively.

kinship verification; Facial Images; Feature Extraction; Histograms; Two-Dimensional Discrete Wavelet Transform

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Wavelet Transform-Based Feature Extraction for Face Kinship Verification

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