Robust regression and outlier detection
Robust regression and outlier detection
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Face Recognition System Using Local Autocorrelations and Multiscale Integration
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Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Fast and Accurate Face Detector Based on Neural Networks
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
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Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fisher Light-Fields for Face Recognition across Pose and Illumination
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FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
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Pose-Invariant Face Recognition with Parametric Linear Subspaces
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Face recognition: component-based versus global approaches
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Face recognition: A literature survey
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Robust Real-Time Face Detection
International Journal of Computer Vision
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Supervised Learning Framework for Generic Object Detection in Images
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Illuminating light field: image-based face recognition across illuminations and poses
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Intra-personal kernel space for face recogni
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A framework for 3d object recognition using the kernel constrained mutual subspace method
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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This paper presents a pose independent classification method from a small number of training samples based on kernel principal component analysis (KPCA) of local parts. Pose changes induce large non-linear variation in feature space of global features. Therefore, conventional methods require multiple poses in training. However, the influence of pose changes in local features is less than that in global features because the global configuration is much influenced. The difference of distributions of local parts cropped from different poses is not so large. If the distribution of local parts cropped from typical poses is modeled, it is robust to unknown poses. Since the distribution of local parts is non-linear, KPCA is used to model the feature space specialized of each class. Class-featuring information compression (CLAFIC) is used to compute the similarity with subspace. In CLAFIC of KPCA, the similarity with certain class is computed by the weighted sum of the similarities with training local parts. Since many local parts are cropped from the input, voting, summation, and median rules are used to combine the similarities of all local parts. Robustness to pose variation is evaluated using the face images of five poses of 300 subjects. Although only frontal and profile views are used in training, the recognition rates to unknown poses are more than 90%. Effectiveness is shown by the comparison with linear PCA of local parts and global features based methods. In addition, the proposed method can be applied easily to the recognition problem of various kinds of 3D objects because it does not require many poses in training or preprocessing such as accurate correspondence between images. The robustness to pose variation and ease of applications are demonstrated using COIL 100 database.