摘要：This work presents a Multi-Resolution Discrete Cosine Transform (MDCT) fusion techniqueFusion Feature-Level Face Recognition Model (FFLFRM) comprising face detection, feature extraction,feature fusion, and face classification. It detects core facial characteristics as well as local and globalfeatures utilizing Local Binary Pattern (LBP) and Principal Component Analysis (PCA) extraction.MDCT fusion technique was applied, followed by Artificial Neural Network (ANN) classification.Model testing used 10,000 faces derived from the Olivetti Research Laboratory (ORL) library. Modelperformance was evaluated in comparison with three state-of-the-art models depending on FrequencyPartition (FP), Laplacian Pyramid (LP) and Covariance Intersection (CI) fusion techniques, in terms ofimage features (low-resolution issues and occlusion) and facial characteristics (pose, and expressionper se and in relation to illumination). The MDCT-based model yielded promising recognition results,with a 97.70% accuracy demonstrating effectiveness and robustness for challenges. Furthermore,this work proved that the MDCT method used by the proposed FFLFRM is simpler, faster, andmore accurate than the Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT) and DiscreteWavelet Transform (DWT). As well as that it is an effective method for facial real-life applications.
