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Mixed-norm sparse representation for multi view face recognition
journal contribution
posted on 2015-09-01, 00:00 authored by Xin Zhang, D Pham, Svetha VenkateshSvetha Venkatesh, W Liu, Quoc-Dinh PhungFace 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.
History
Journal
Pattern recognitionVolume
48Issue
9Pagination
2935 - 2946Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0031-3203eISSN
1873-5142Language
engPublication classification
C1 Refereed article in a scholarly journal; C Journal articleCopyright notice
2015, ElsevierUsage metrics
Categories
Keywords
multi-pose face recognitionsparse representatation classificationADMMmulti-task learningjoint dynamic sparse representationclassificationunsupervised learningconvex optimizationrobust face recoginitionGroup sparse representationJoint dynamic sparse representation classificationRobust face recognitionSparse representation classificationScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicComputer ScienceEngineeringSIGNAL RECOVERYInformation SystemsArtificial Intelligence and Image Processing
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