Performance analysis of weighted low rank model with sparse image histograms for face recognition under lowlevel illumination and occlusion

In a broad range of computer vision applications, the purpose of Low-rank matrix approximation (LRMA) models is to recover the underlying low-rank matrix from its degraded observation. The latest LRMA methods - Robust Principal Component Analysis (RPCA) resort to using the nuclear norm minimization...

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Veröffentlicht in:	arXiv.org 2020-07
Hauptverfasser:	Sridhar, K V, Hemadri, Raghu vamshi
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Approximation Computer vision Face recognition Histograms Illumination Mathematical analysis Object recognition Occlusion Optimization Principal components analysis
Online-Zugang:	Volltext
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