Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Based on Nearest Linear Combinations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Journal of Cognitive Neuroscience
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A descriptor combining MHI and PCOG for human motion classification
Proceedings of the ACM International Conference on Image and Video Retrieval
Linear Regression for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering appearances of objects under varying illumination conditions
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A regularized correntropy framework for robust pattern recognition
Neural Computation
Maximum Correntropy Criterion for Robust Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust regression for face recognition
Pattern Recognition
Beyond sparsity: The role of L1-optimizer in pattern classification
Pattern Recognition
Human action segmentation and recognition via motion and shape analysis
Pattern Recognition Letters
Is face recognition really a Compressive Sensing problem?
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Robust sparse coding for face recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Fast Solution of -Norm Minimization Problems When the Solution May Be Sparse
IEEE Transactions on Information Theory
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
Sparse representation or collaborative representation: Which helps face recognition?
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this work, we propose a linear representation based face recognition (FR) method incorporating locality information from both spatial features and training samples. Instead of holistic face images, the proposed method is conducted on the spatial pyramid local patches, which are aggregated by a Bayesian based fusion method. The locality constraint on the representation coefficients leads to an approximately sparse representation, which effectively explores the discriminative nature of spatial local features. Different from the sparse representation based classification (SRC) exposing an @?^1-norm constraint on the coefficients, the proposed locality constrained representation based classification (LCRC) is formulated with a computationally efficient @?^2-norm. The proposed method is robust to two crucial problems in face recognition: occlusion and lack of training data. A simple locality based concentration index (LCI) is defined to measure the reliability of each local patch, by which not only the heavily corrupted patches but also the less discriminant ones are rejected. Due to the use of both local patches and the locality constraint, less training data are required by the proposed method. Based on the locality constrained representation, we present three algorithms which outperform the state-of-the-art on the AR and Extended Yale B datasets for both the occlusion and single sample per person (SSPP) problems.