Sparse Representation Classifier Steered Discriminative Projection With Applications to Face Recognition

A sparse representation-based classifier (SRC) is developed and shows great potential for real-world face recognition. This paper presents a dimensionality reduction method that fits SRC well. SRC adopts a class reconstruction residual-based decision rule, we use it as a criterion to steer the desig...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2013-07, Vol.24 (7), p.1023-1035
Main Authors: Yang, Jian, Chu, Delin, Zhang, Lei, Xu, Yong, Yang, Jingyu
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Biometric Identification - methods
Classifiers
Computer science
control theory
systems
Data processing. List processing. Character string processing
Databases, Factual
Detection, estimation, filtering, equalization, prediction
Dimensionality reduction
Discriminant Analysis
Exact sciences and technology
Face
Face - anatomy & histology
Face recognition
Facial Expression
Feature extraction
Humans
Image Processing, Computer-Assisted
Information systems. Data bases
Information, signal and communications theory
Memory organisation. Data processing
Neural networks
Optimization
Pattern Recognition, Visual
Pattern recognition. Digital image processing. Computational geometry
Projection
Reconstruction
Representations
Signal and communications theory
Signal, noise
Software
Sparse matrices
sparse representation
Studies
Telecommunications and information theory
Training
Vectors
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Summary:A sparse representation-based classifier (SRC) is developed and shows great potential for real-world face recognition. This paper presents a dimensionality reduction method that fits SRC well. SRC adopts a class reconstruction residual-based decision rule, we use it as a criterion to steer the design of a feature extraction method. The method is thus called the SRC steered discriminative projection (SRC-DP). SRC-DP maximizes the ratio of between-class reconstruction residual to within-class reconstruction residual in the projected space and thus enables SRC to achieve better performance. SRC-DP provides low-dimensional representation of human faces to make the SRC-based face recognition system more efficient. Experiments are done on the AR, the extended Yale B, and PIE face image databases, and results demonstrate the proposed method is more effective than other feature extraction methods based on the SRC.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2013.2249088