Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary

Sparse Representation-Based Classification (SRC) is a face recognition breakthrough in recent years which has successfully addressed the recognition problem with sufficient training images of each gallery subject. In this paper, we extend SRC to applications where there are very few, or even a singl...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 34; H. 9; S. 1864 - 1870
Hauptverfasser: Deng, Weihong, Hu, Jiani, Guo, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Los Alamitos, CA IEEE 01.09.2012
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Applied sciences
Approximation
Artificial intelligence
Biometric Identification - methods
Classification
Computer science; control theory; systems
Databases, Factual
Detection, estimation, filtering, equalization, prediction
Dictionaries
Error analysis
Exact sciences and technology
Face
Face - anatomy & histology
Face recognition
Facial Expression
feature extraction
Female
Galleries
Humans
Illumination
Image Processing, Computer-Assisted - methods
Information, signal and communications theory
Lighting
Male
Pattern recognition. Digital image processing. Computational geometry
Signal and communications theory
Signal, noise
sparse representation
Studies
Telecommunications and information theory
Training
undersampled problem
undersampled problem
Computer vision
Dictionaries
Image processing
Face recognition
Autoregressive model
Regression analysis
Pattern recognition
Luminance
Experimental result
feature extraction
Classification
Facies
Database
Selection criterion
Illumination
Sparse representation
Large scale
Age
Pattern extraction
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Sparse Representation-Based Classification (SRC) is a face recognition breakthrough in recent years which has successfully addressed the recognition problem with sufficient training images of each gallery subject. In this paper, we extend SRC to applications where there are very few, or even a single, training images per subject. Assuming that the intraclass variations of one subject can be approximated by a sparse linear combination of those of other subjects, Extended Sparse Representation-Based Classifier (ESRC) applies an auxiliary intraclass variant dictionary to represent the possible variation between the training and testing images. The dictionary atoms typically represent intraclass sample differences computed from either the gallery faces themselves or the generic faces that are outside the gallery. Experimental results on the AR and FERET databases show that ESRC has better generalization ability than SRC for undersampled face recognition under variable expressions, illuminations, disguises, and ages. The superior results of ESRC suggest that if the dictionary is properly constructed, SRC algorithms can generalize well to the large-scale face recognition problem, even with a single training image per class.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2012.30