A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video tomography: an efficient method for camerawork extraction and motion analysis
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
Face Recognition from Long-Term Observations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Recent Advances in Image Morphing
CGI '96 Proceedings of the 1996 Conference on Computer Graphics International
Illumination Cones for Recognition under Variable Lighting: Faces
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
A System Identification Approach for Video-based Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Online Learning of Probabilistic Appearance Manifolds for Video-Based Recognition and Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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Pose and illumination variation has been considered the major cause of poor recognition results in automatic face recognition as compared to other biometrics. With the advent of video based face recognition a decade ago we were presented with some new opportunities, algorithms were developed to take advantage of the abundance of data and behavioral aspect of recognition. But this modality introduced some new challenges also, one of them was the variation introduced by speech. In this paper we present a novel method for handling this variation by using temporal normalization based on lip motion. Evaluation was carried out by comparing face recognition results from original non-normalized videos and normalized videos.