Human face recognition and the face image set's topology
CVGIP: Image Understanding
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
Mixtures of probabilistic principal component analyzers
Neural Computation
How Should We RepresentFaces for Automatic Recognition?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Merging and Splitting Eigenspace Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Variational Framework for Retinex
International Journal of Computer Vision
On Affine Invariant Clustering and Automatic Cast Listing in Movies
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Face Recognition from Long-Term Observations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
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
Unified Subspace Analysis for Face Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Filtering Using a Tree-Based Estimator
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Probabilistic recognition of human faces from video
Computer Vision and Image Understanding - Special issue on Face recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
Robust Real-Time Face Detection
International Journal of Computer Vision
Face Recognition with Image Sets Using Manifold Density Divergence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Automatic Face Recognition for Film Character Retrieval in Feature-Length Films
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A New Look at Filtering Techniques for Illumination Invariance in Automatic Face Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Face recognition under varying lighting conditions using self quotient image
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Video-based face recognition using adaptive hidden markov models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Person spotting: video shot retrieval for face sets
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Comparison of face matching techniques under pose variation
Proceedings of the 6th ACM international conference on Image and video retrieval
A methodology for rapid illumination-invariant face recognition using image processing filters
Computer Vision and Image Understanding
Object-Video Streams for Preserving Privacy in Video Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Unfolding a face: from singular to manifold
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Remote identification of faces: Problems, prospects, and progress
Pattern Recognition Letters
Multi-eigenspace learning for video-based face recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Dictionary-based face recognition from video
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
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In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. In particular there are three areas of novelty: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation, learnt offline, to generalize in the presence of extreme illumination changes; (ii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve invariance to unseen head poses; and (iii) we introduce an accurate video sequence “reillumination” algorithm to achieve robustness to face motion patterns in video. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our system consistently demonstrated a nearly perfect recognition rate (over 99.7%), significantly outperforming state-of-the-art commercial software and methods from the literature.