The FERET Verification Testing Protocol for Face Recognition Algorithms
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
A Comparative Analysis of Face Recognition Performance with Visible and Thermal Infrared Imagery
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
PCA-Based Face Recognition in Infrared Imagery: Baseline and Comparative Studies
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Face Recognition in Hyperspectral Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusion of Visual and Thermal Signatures with Eyeglass Removal for Robust Face Recognition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 8 - Volume 08
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recent advances in visual and infrared face recognition: a review
Computer Vision and Image Understanding
Multi-Sensory Face Biometric Fusion (for Personal Identification)
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
An Indoor and Outdoor, Multimodal, Multispectral and Multi-Illuminant Database for Face Recognition
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Multiscale Fusion of Visible and Thermal IR Images for Illumination-Invariant Face Recognition
International Journal of Computer Vision
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparsity preserving projections with applications to face recognition
Pattern Recognition
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Robust Discriminant Analysis Based on Nonparametric Maximum Entropy
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning
IEEE Transactions on Neural Networks
Learning with l1-graph for image analysis
IEEE Transactions on Image Processing
Studies on hyperspectral face recognition in visible spectrum with feature band selection
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
IEEE Transactions on Multimedia
Reconstruction and Recognition of Tensor-Based Objects With Concurrent Subspaces Analysis
IEEE Transactions on Circuits and Systems for Video Technology
MPCA: Multilinear Principal Component Analysis of Tensor Objects
IEEE Transactions on Neural Networks
l2, 1 Regularized correntropy for robust feature selection
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Face recognition by discriminant analysis with gabor tensor representation
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Eyeglasses removal of thermal image based on visible information
Information Fusion
| Hi-index | 0.01 |
Face recognition using different imaging modalities has become an area of growing interest. A large number of multispectral face recognition algorithms/systems have been proposed in last decade. How to fuse features of different spectrum has still been a crucial problem for face recognition. To address this problem, we propose a sparse tensor embedding (STE) algorithm which represents a multispectral image as a third-order tensor. STE constructs sparse neighborhoods and the corresponding weights of the tensor. One advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. STE iteratively obtains one spectral space transformation matrix through preserving the sparse neighborhoods. Due to sparse representation, STE can not only keep the underlying spatial structure of multispectral images but also enhance robustness. The experiments on multispectral face databases, Equinox and PolyU-HSFD face databases, show that the performance of the proposed method outperform that of the state-of-the-art algorithms.