کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
13429068 | 1842331 | 2020 | 18 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Cross-resolution face recognition with pose variations via multilayer locality-constrained structural orthogonal procrustes regression
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Cross-resolution face recognition with pose variations via multilayer locality-constrained structural orthogonal procrustes regression Cross-resolution face recognition with pose variations via multilayer locality-constrained structural orthogonal procrustes regression](/preview/png/13429068.png)
چکیده انگلیسی
In real video surveillance scenes, the extracted face regions generally have low-resolution (LR) and are sensitive to pose and illumination variations; these flaws undoubtedly degrade the subsequent recognition task. To overcome these challenges, we propose an approach named multilayer locality-constrained structural orthogonal Procrustes regression (MLCSOPR). The proposed MLCSOPR not only learns the pose-robust discriminative representation features to reduce the resolution gap between the LR image space and the high-resolution (HR) one but also strengthens the consistency between the LR and HR image space. In particular, several contributions are made in this paper: (i) Inspired by the orthogonal Procrustes problem (OPP), a matrix approximation is exploited to find an optimal correction between two data matrices. (ii) The nuclear norm constraint is applied to the reconstruction error to maintain the structural property. (iii) Based on the abovementioned learned resolution-robust representation features, a linear regression-based classification strategy is adopted to recognize the LR input face images. Experiments on commonly used face databases have shown the effectiveness of the proposed method on cross-resolution face matching with pose variations.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 506, January 2020, Pages 19-36
Journal: Information Sciences - Volume 506, January 2020, Pages 19-36
نویسندگان
Gao Guangwei, Yu Yi, Yang Meng, Chang Heyou, Huang Pu, Yue Dong,