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Mixed-norm sparse representation for multi view face recognition

Zhang, Xin, Pham, Duc-Son, Venkatesh, Svetha, Liu, Wanquan and Phung, Dinh 2015, Mixed-norm sparse representation for multi view face recognition, Pattern recognition, vol. 48, no. 9, pp. 2935-2946, doi: 10.1016/j.patcog.2015.02.022.

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Title Mixed-norm sparse representation for multi view face recognition
Author(s) Zhang, Xin
Pham, Duc-Son
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Liu, Wanquan
Phung, DinhORCID iD for Phung, Dinh
Journal name Pattern recognition
Volume number 48
Issue number 9
Start page 2935
End page 2946
Total pages 12
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-09
ISSN 0031-3203
Summary Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation is too large or missing, 'shared information' may not be properly extracted, leading to poor recognition results. In this paper, we propose a novel method for face recognition with multiple view images to overcome the large pose variation and missing pose issue. By introducing a novel mixed norm, the proposed method automatically selects candidates from the gallery to best represent a group of highly correlated face images in a query set to improve classification accuracy. This mixed norm combines the advantages of both sparse representation based classification (SRC) and joint sparse representation based classification (JSRC). A trade off between the ℓ1-norm from SRC and ℓ2,1-norm from JSRC is introduced to achieve this goal. Due to this property, the proposed method decreases the influence when a face image is unseen and has large pose variation in the recognition process. And when some face images with a certain degree of unseen pose variation appear, this mixed norm will find an optimal representation for these query images based on the shared information induced from multiple views. Moreover, we also address an open problem in robust sparse representation and classification which is using ℓ1-norm on the loss function to achieve a robust solution. To solve this formulation, we derive a simple, yet provably convergent algorithm based on the powerful alternative directions method of multipliers (ADMM) framework. We provide extensive comparisons which demonstrate that our method outperforms other state-of-the-arts algorithms on CMU-PIE, Yale B and Multi-PIE databases for multi-view face recognition.
Language eng
DOI 10.1016/j.patcog.2015.02.022
Field of Research 080109 Pattern Recognition and Data Mining
0899 Other Information And Computing Sciences
0906 Electrical And Electronic Engineering
0801 Artificial Intelligence And Image Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
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Document type: Journal Article
Collection: Centre for Pattern Recognition and Data Analytics
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