On multiple imputation-based reconstruction of degraded faces and recognition in multiple constrained environments
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Date
2023
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Scientific African
Abstract
Recognition of degraded frontal face images acquired under occlusion constraints remain
challenging despite the plethora of reconstruction mechanisms. Though recent works have lever aged on some imputation mechanisms in this regard, their robustness in multiple constrained
environments may not be guaranteed and may be affected by the choice of pre-processing
mechanism. This paper proposes enhancement mechanisms that augment or complement the
use of three (3) multiple imputation mechanisms for facial reconstruction in the presence of
multiple constraints (10% and 20% occlusions and varying facial expressions). Specifically, we
propose the use of a Discrete Cosine Transform-based (DCT) denoising or a Discrete Wavelet based denoising following Histogram Equalization (HE-DWT) of the reconstructed face images
prior to recognition. Experimental results showed that the proposed augmented enhancements
improved significantly the recognition rates (90.63% & 91.15% and 86.98% & 85.94% for DCT
and HE-DWT at 10% and 20% occlusion levels respectively for Missforest de-occluded face
images) as compared with DWT in recognizing degraded frontal face images under moderately
low levels of occlusions and varying expressions.
Description
Research Article
Keywords
Multiple imputation methods, Face recognition, Multiple constraints, Occlusions, Discrete cosine transform