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Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition

delete2017-08-01
delete76
PRE
AI
J
Jiwen Lu *
G
Gang Wang
周杰 (Jie Zhou)
DOI:10.1109/TIP.2017.2713940delete
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摘要

摘要

En 中文
In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set-based face recognition, where each training and testing example contains a set of face images, which were captured from different variations of pose, illumination, expression, resolution, and motion. While a variety of feature learning and dictionary learning methods have been proposed in recent years and some of them have been successfully applied to image set-based face recognition, most of them learn features and dictionaries for facial image sets individually, which may not be powerful enough because some discriminative information for dictionary learning may be compromised in the feature learning stage if they are applied sequentially, and vice versa. To address this, we propose a SFDL method to learn discriminative features and dictionaries simultaneously from raw face pixels so that discriminative information from facial image sets can be jointly exploited by a one-stage learning procedure. To better exploit the nonlinearity of face samples from different image sets, we propose a deep SFDL (D-SFDL) method by jointly learning hierarchical non-linear transformations and class-specific dictionaries to further improve the recognition performance. Extensive experimental results on five widely used face data sets clearly shows that our SFDL and D-SFDL achieve very competitive or even better performance with the state-of-the-arts.
Keyword:
Face recognition
feature learning
dictionary learning
deep learning
image set classification
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期刊

IEEE Transactions on Image Processing 封面图
IEEE Transactions on Image Processing
IF:
13.7
论文数:
1.0W
被引数:
8.4W

机构

A
alibaba group
学者数:
1.1K
论文数: 789
被引数: 0
T
tsinghua university
学者数:
11.9W
论文数: 10.0W
被引数: 137
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