The nature of statistical learning theory
The nature of statistical learning theory
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Evidence in Multimodal Personal Identity Recognition Systems
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Component-based face recognition with 3D morphable models
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Face detection and facial component extraction by wavelet decomposition and support vector machines
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
A Component-based Framework for Face Detection and Identification
International Journal of Computer Vision
Weighted ensemble boosting for robust activity recognition in video
Machine Graphics & Vision International Journal
Face recognition under occlusions and variant expressions with partial similarity
IEEE Transactions on Information Forensics and Security
Making action recognition robust to occlusions and viewpoint changes
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Expression recognition in videos using a weighted component-based feature descriptor
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Facial expression recognition from near-infrared videos
Image and Vision Computing
Polymorphous facial trait code
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Discriminant phase component for face recognition
Journal of Electrical and Computer Engineering
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In this paper we explore different strategies for classifier combination within the framework of component-based face recognition. In our current system, the gray values of facial components are concatenated to a single feature vector which is then fed into the face recognition classifier. As an alternative, we suggest to train recognition classifiers on each of the components separately and then combine their outputs using the following three strategies: voting, sum of outputs, and product of outputs. We also propose a novel Bayesian method which weights the classifier outputs prior to their combination. In experiments on two face databases, we evaluate the different strategies and compare them to our existing recognition system.