Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tensor Decompositions and Applications
SIAM Review
Multiresolution face recognition
Image and Vision Computing
Extracting multiple features in the CID Color Space for face recognition
IEEE Transactions on Image Processing
Anthropometric 3D Face Recognition
International Journal of Computer Vision
Using random subspace to combine multiple features for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A survey of multilinear subspace learning for tensor data
Pattern Recognition
PCA-based image recombination for multimodal 2D+3D face recognition
Image and Vision Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Multilinear Discriminant Analysis for Face Recognition
IEEE Transactions on Image Processing
Feature selection from high-order tensorial data via sparse decomposition
Pattern Recognition Letters
Relaxed collaborative representation for pattern classification
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Fisher Discrimination Dictionary Learning for sparse representation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Sparse representation or collaborative representation: Which helps face recognition?
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A dictionary learning approach for classification: separating the particularity and the commonality
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
| Hi-index | 0.00 |
Recent research emphasizes more on analyzing multiple features to improve face recognition (FR) performance. One popular scheme is to extend the sparse representation based classification framework with various sparse constraints. Although these methods jointly study multiple features through the constraints, they just process each feature individually such that they overlook the possible high-level relationship among different features. It is reasonable to assume that the low-level features of facial images, such as edge information and smoothed/low-frequency image, can be fused into a more compact and more discriminative representation based on the latent high-level relationship. FR on the fused features is anticipated to produce better performance than that on the original features, since they provide more favorable properties. Focusing on this, we propose two different strategies which start from fusing multiple features and then exploit the dictionary learning (DL) framework for better FR performance. The first strategy is a simple and efficient two-step model, which learns a fusion matrix from training face images to fuse multiple features and then learns class-specific dictionaries based on the fused features. The second one is a more effective model requiring more computational time that learns the fusion matrix and the class-specific dictionaries simultaneously within an iterative optimization procedure. Besides, the second model considers to separate the shared common components from class-specified dictionaries to enhance the discrimination power of the dictionaries. The proposed strategies, which integrate multi-feature fusion process and dictionary learning framework for FR, realize the following goals: (1) exploiting multiple features of face images for better FR performances; (2) learning a fusion matrix to merge the features into a more compact and more discriminative representation; (3) learning class-specific dictionaries with consideration of the common patterns for better classification performance. We perform a series of experiments on public available databases to evaluate our methods, and the experimental results demonstrate the effectiveness of the proposed models.