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IEEE Transactions on Pattern Analysis and Machine Intelligence
Constructing Facial Identity Surfaces for Recognition
International Journal of Computer Vision
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ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A Temporal Network of Support Vector Machine Classifiers for the Recognition of Visual Speech
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
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SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
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SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
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WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Journal of Computer Science and Technology
Three-dimensional view-invariant face recognition using a hierarchical pose-normalization strategy
Machine Vision and Applications
Multi-view face and eye detection using discriminant features
Computer Vision and Image Understanding
Synergistic Face Detection and Pose Estimation with Energy-Based Models
The Journal of Machine Learning Research
A support vector machine-based dynamic network for visual speech recognition applications
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A two-stage head pose estimation framework and evaluation
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View influence analysis and optimization for multiview face recognition
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Identity Management in Face Recognition Systems
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IEEE Transactions on Circuits and Systems for Video Technology
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CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
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FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Face recognition under variable lighting using harmonic image exemplars
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Pattern Recognition Letters
Coarse head pose estimation of construction equipment operators to formulate dynamic blind spots
Advanced Engineering Informatics
Face detection using kernel PCA and imbalanced SVM
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Robust multi-view face detection using error correcting output codes
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Robust automatic human identification using face, mouth, and acoustic information
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
An integrated two-stage framework for robust head pose estimation
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
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IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
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ICEB'10 Proceedings of the Third international conference on Ethics and Policy of Biometrics and International Data Sharing
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ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
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A Support Vector Machine based multi-view face detection and recognition framework is described in this paper. Face detection is carried out by constructing several detectors, each of them in charge of one specific view. The symmetrical property of face images is employed to simplify the complexity of the modeling. The estimation of head pose, which is achieved by using the Support Vector Regression technique, provides crucial information for choosing the appropriate face detector. This helps to improve the accuracy and reduce the computation in multi-view face detection compared to other methods. For video sequences, further computational reduction can be achieved by using Pose Change Smoothing strategy. When face detectors find a face in frontal view, a Support Vector Machine based multi-class classifier is activated for face recognition. All the above issues are integrated under a Support Vector Machine framework. Test results on four video sequences are presented, among them, detection rate is above 95%, recognition accuracy is above 90%, average pose estimation error is around 10 degs, and the full detection and recognition speed is up to 4 frames/second on a PentiumII300 PC.