Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Recognition by Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Robust Real-Time Face Detection
International Journal of Computer Vision
A decision based one-against-one method for multi-class support vector machine
Pattern Analysis & Applications
Support Vector Machine with Local Summation Kernel for Robust Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Journal of Cognitive Neuroscience
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Face Recognition Based on Normalization and the Phase Spectrum of the Local Part of an Image
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Estimation of Object Position Based on Color and Shape Contextual Information
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
An MRF-based kernel method for nonlinear feature extraction
Image and Vision Computing
Local normalized linear summation kernel for fast and robust recognition
Pattern Recognition
On-line evolving image classifiers and their application to surface inspection
Image and Vision Computing
Expert Systems with Applications: An International Journal
Feature Extraction Using a Complete Kernel Extension of Supervised Graph Embedding
Neural Processing Letters
A sub-block-based eigenphases algorithm with optimum sub-block size
Knowledge-Based Systems
Template matching of occluded object under low PSNR
Digital Signal Processing
Face recognition using Gabor-based direct linear discriminant analysis and support vector machine
Computers and Electrical Engineering
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This paper presents the use of Support Vector Machine (SVM) with local Gaussian summation kernel for robust face recognition under partial occlusion. In recent years, the effectiveness of SVM and local features has been reported. However, because conventional methods apply one kernel to global features and global features are influenced easily by noise or occlusion, the conventional methods are not robust to occlusion. The recognition method based on local features, however, is robust to occlusion because partial occlusion affects only specific local features. In order to utilize this property of local features in SVM, local kernels are applied to local features. The use of local kernels in SVM requires local kernel integration. The summation of local kernels is used as the integration method in this study. The effectiveness and robustness of the proposed method are shown by comparison with global kernel based SVM. The recognition rate of the proposed method is high under large occlusion, whereas the recognition rate of the SVM with the global Gaussian kernel decreases drastically. Furthermore, we investigate the robustness to practical occlusion in the real world using the AR face database. Although only face images with non-occlusion are used for training, faces wearing sunglasses or a scarf are classified with high accuracy.