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De-occlusion and recognition of frontal face images: a comparative study of multiple imputation methods

Mensah, Joseph Agyapong ; Nortey, Ezekiel N. N. ; Ocran, Eric ; Iddi, Samuel ; Asiedu, Louis

Journal of Big Data, 2024-12, Vol.11 (1), p.60-22 [Periódico revisado por pares]

Cham: Springer International Publishing

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Título:
De-occlusion and recognition of frontal face images: a comparative study of multiple imputation methods
Autor: Mensah, Joseph Agyapong ; Nortey, Ezekiel N. N. ; Ocran, Eric ; Iddi, Samuel ; Asiedu, Louis
Assuntos: Absolute mean brightness error ; Algorithms ; Artificial intelligence ; Big Data ; Biometry ; Communications Engineering ; Comparative analysis ; Comparative studies ; Computational Science and Engineering ; Computer Science ; Data Mining and Knowledge Discovery ; Database Management ; Discrete Wavelet Transform ; Expression-variant face images ; Face recognition ; Image acquisition ; Image degradation ; Image enhancement ; Image quality ; Image restoration ; Information Storage and Retrieval ; Machine learning ; Mathematical Applications in Computer Science ; Methods ; Modules ; Multiple constraint ; Multiple Imputation with chain equations ; Networks ; Occlusion ; Occlusions ; Peak signal to noise ratio ; Performance evaluation ; Principal components analysis ; Robustness (mathematics) ; Signal to noise ratio ; Singular value decomposition
É parte de: Journal of Big Data, 2024-12, Vol.11 (1), p.60-22
Descrição: Increasingly, automatic face recognition algorithms have become necessary with the development and extensive use of face recognition technology, particularly in the era of machine learning and artificial intelligence. However, the presence of unconstrained environmental conditions degrades the quality of acquired face images and may deteriorate the performance of many classical face recognition algorithms. Due to this backdrop, many researchers have given considerable attention to image restoration and enhancement mechanisms, but with minimal focus on occlusion-related and multiple-constrained problems. Although occlusion robust face recognition modules, via sparse representation have been explored, they require a large number of features to achieve correct computations and to maximize robustness to occlusions. Therefore, such an approach may become deficient in the presence of random occlusions of relatively moderate magnitude. This study assesses the robustness of Principal Component Analysis and Singular Value Decomposition using Discrete Wavelet Transformation for preprocessing and city block distance for classification (DWT-PCA/SVD-L1) face recognition module to image degradations due to random occlusions of varying magnitudes (10% and 20%) in test images acquired with varying expressions. Numerical evaluation of the performance of the DWT-PCA/SVD-L1 face recognition module showed that the use of the de-occluded faces for recognition enhanced significantly the performance of the study recognition module at each level (10% and 20%) of occlusion. The algorithm attained the highest recognition rate of 85.94% and 78.65% at 10% and 20% occlusions respectively, when the MICE de-occluded face images were used for recognition. With the exception of Entropy where MICE de-occluded face images attained the highest average value, the MICE and RegEM result in images of similar quality as measured by their Absolute mean brightness error (AMBE) and peak signal to noise ratio (PSNR). The study therefore recommends MICE as a suitable imputation mechanism for de-occlusion of face images acquired under varying expressions.
Editor: Cham: Springer International Publishing
Idioma: Inglês

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De-occlusion and recognition of frontal face images: a comparative study of multiple imputation methods

Mensah, Joseph Agyapong ; Nortey, Ezekiel N. N. ; Ocran, Eric ; Iddi, Samuel ; Asiedu, Louis

Cham: Springer International Publishing

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