Sparse and Dense Hybrid Representation via Dictionary Decomposition for Face Recognition

Sparse representation provides an effective tool for classification under the conditions that every class has sufficient representative training samples and the training data are uncorrupted. These conditions may not hold true in many practical applications. Face identification is an example where w...

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Vydáno v:IEEE transactions on pattern analysis and machine intelligence Ročník 37; číslo 5; s. 1067 - 1079
Hlavní autoři: Jiang, Xudong, Lai, Jian
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.05.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Biometric Identification - methods
Databases, Factual
Dictionaries
Face
Face - anatomy & histology
Face recognition
Feature extraction
Humans
Image Processing, Computer-Assisted - methods
Sparse matrices
Training
Training data
Vectors
face recognition
low-rank matrix recovery
Sparse representation
dictionary learning
classification
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Shrnutí:Sparse representation provides an effective tool for classification under the conditions that every class has sufficient representative training samples and the training data are uncorrupted. These conditions may not hold true in many practical applications. Face identification is an example where we have a large number of identities but sufficient representative and uncorrupted training images cannot be guaranteed for every identity. A violation of the two conditions leads to a poor performance of the sparse representation-based classification (SRC). This paper addresses this critic issue by analyzing the merits and limitations of SRC. A sparse- and dense-hybrid representation (SDR) framework is proposed in this paper to alleviate the problems of SRC. We further propose a procedure of supervised low-rank (SLR) dictionary decomposition to facilitate the proposed SDR framework. In addition, the problem of the corrupted training data is also alleviated by the proposed SLR dictionary decomposition. The application of the proposed SDR-SLR approach in face recognition verifies its effectiveness and advancement to the field. Extensive experiments on benchmark face databases demonstrate that it consistently outperforms the state-of-the-art sparse representation based approaches and the performance gains are significant in most cases.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2014.2359453