IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Machine Learning
Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Random Sampling for Subspace Face Recognition
International Journal of Computer Vision
Automatic Cast Listing in Feature-Length Films with Anisotropic Manifold Space
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Boosted manifold principal angles for image set-based recognition
Pattern Recognition
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Adaptive mixtures of local experts
Neural Computation
Integrated Detect-Track Framework for Multi-view Face Detection in Video
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Boosting constrained mutual subspace method for robust image-set based object recognition
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Score normalization in multimodal biometric systems
Pattern Recognition
The kernel orthogonal mutual subspace method and its application to 3D object recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
A multiscale retinex for bridging the gap between color images and the human observation of scenes
IEEE Transactions on Image Processing
Multi-scale local binary pattern histograms for face recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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In set-based face recognition, each set of face images is often represented as a linear/nonlinear manifold and the Principal Angles (PA) or Kernel PAs are exploited to measure the (dis-)similarity between manifolds. This work systemically evaluates the effect of using different face image representations and different types of kernels in the KPA setup and presents a novel way of randomised learning of manifolds for setbased face recognition. First, our experiments show that sparse features such as Local Binary Patterns and Gabor wavelets significantly improve the accuracy of PA methods over 'pixel intensity'. Combining different features and types of kernels at their best hyper-parameters in a multiple classifier system has further yielded the improved accuracy. Based on the encouraging results, we propose a way of randomised learning of kernel types and hyper-parameters by the set-based Randomised Decision Forests. We observed that the proposed method with linear kernels efficiently competes with those of nonlinear kernels. Further incorporation of discriminative information by constrained subspaces in the proposed method has effectively improved the accuracy. In the experiments over the challenging data sets, the proposed methods improve the accuracy of the standard KPA method by about 35 percent and outperform the Support Vector Machine with the set-kernels manually tuned.