Multi-Resolution Discrete Cosine Transform Fusion Technique Face Recognition Model

Bader M. AlFawwaz, Atallah AL-Shatnawi, Faisal Al-Saqqar and Mohammad Nusir
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Bader M. AlFawwaz: Department of Information Systems, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan
Atallah AL-Shatnawi: Department of Information Systems, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan
Faisal Al-Saqqar: Department of Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan
Mohammad Nusir: CBM Integrated Software Inc. (CBMIS), San Diego, CA 92101, USA

Data, 2022, vol. 7, issue 6, 1-15

Abstract: This work presents a Multi-Resolution Discrete Cosine Transform (MDCT) fusion technique Fusion 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 global features 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. Model performance was evaluated in comparison with three state-of-the-art models depending on Frequency Partition (FP), Laplacian Pyramid (LP) and Covariance Intersection (CI) fusion techniques, in terms of image features (low-resolution issues and occlusion) and facial characteristics (pose, and expression per 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, and more accurate than the Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT). As well as that it is an effective method for facial real-life applications.

Keywords: feature fusion; face recognition; Laplacian Pyramid; multi-resolution discrete cosine transform (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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