The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Support Vector Regression and Classification Based Multi-View Face Detection and Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Robust Real-Time Face Detection
International Journal of Computer Vision
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
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
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning Hierarchical Models of Scenes, Objects, and Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
An Efficient Search Method Based on Dynamic Attention Map by Ising Model
IEICE - Transactions on Information and Systems
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Local normalized linear summation kernel for fast and robust recognition
Pattern Recognition
Action Recognition Based on Non-parametric Probability Density Function Estimation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
A review of recent advances in 3D ear- and expression-invariant face biometrics
ACM Computing Surveys (CSUR)
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This paper proposes a view independent face detection method based on horizontal rectangular features, and accuracy improvement by combining kernels of various sizes. Since the view changes of faces induce large variation in appearance in the horizontal direction, local kernels are applied to horizontal rectangular regions to model such appearance changes. Local kernels are integrated by summation, and then used as a summation kernel for support vector machine (SVM). View independence is shown to be realized by the integration of local horizontal rectangular kernels. However, in general, local kernels (features) of various sizes have different similarity measures, such as detailed and rough similarity, and thus their error patterns are different. If the local and global kernels are combined well, the generalization ability is improved. This research demonstrates the comparative effectiveness of combining the global kernel and local kernels of various sizes as a summation kernel for SVM against use of only the global kernel, only the combination of local kernels and Adaboost with SVMs with a kind of local kernel.