Advancing Face Recognition for Low-Resolution with Multi-Linear Side Information-Based Discriminant Analysis

Bellili Sana; Ouamane Abdelmalik; Chouchane Ammar; Bourennane Salah

doi:10.20944/preprints202409.0682.v1

Preprint Communication Version 1 This version is not peer-reviewed

Version 1 : Received: 7 September 2024 / Approved: 9 September 2024 / Online: 9 September 2024 (12:03:45 CEST)

Face recognition is a key computer vision task that focuses on identifying or verifying individuals using their facial features. This task becomes more difficult with low-resolution images, where the reduced pixel count and detail make it harder to extract and match features accurately. In this study, we assess the effectiveness of Multilinear Side-Information-based Discriminant Analysis (MSIDA) on low-resolution images, using the CelebA database as a reference. The system showed strong performance, achieving 90.60% accuracy on high-resolution and 88.23% on low-resolution images, highlighting the robustness and effectiveness of MSIDA.

Face recognitio; Low-resolution; MSIDA; CelebA database

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Advancing Face Recognition for Low-Resolution with Multi-Linear Side Information-Based Discriminant Analysis

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