Abstract
In this paper, we propose a novel approach for low-resolution face recognition, under uncontrolled settings. Our approach first decomposes a multiple of extracted local features into a set of representative basis (low-rank matrix) and sparse error matrix, and then learns a projection matrix based on our proposed sparse-coding-based algorithm, which preserves the sparse structure of the learned low-rank features, in a low-dimensional feature subspace. Then, a coefficient vector, based on linear regression, is computed to determine the similarity between the projected gallery and query image's features. Furthermore, a new morphological pre-processing approach is proposed to improve the visual quality of images. Our experiments were conducted on five available face-recognition datasets, which contain images with variations in pose, facial expressions and illumination conditions. Experiment results show that our method outperforms other state-of–the-art low-resolution face recognition methods in terms of recognition accuracy.
Original language | English |
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Article number | 102590 |
Journal | Journal of Visual Communication and Image Representation |
Volume | 63 |
DOIs | |
Publication status | Published - Aug 2019 |
Keywords
- Face recognition
- Feature fusion
- Linear regression
- Local features
- Low rank approximation
- Sparse coding
ASJC Scopus subject areas
- Signal Processing
- Media Technology
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering