From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Affine Invariant Clustering and Automatic Cast Listing in Movies
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
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
Face Recognition Using Most Discriminative Local and Global Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Discriminative Feature Co-Occurrence Selection for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A pose-wise linear illumination manifold model for face recognition using video
Computer Vision and Image Understanding
Grassmann Registration Manifolds for Face Recognition
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
A face recognition system based on local feature analysis
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Face recognition from video using the generic shape-illumination manifold
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Person spotting: video shot retrieval for face sets
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
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Face recognition from a single image remains an important task in many practical applications and a significant research challenge Some of the challenges are inherent to the problem, for example due to changing lighting conditions Others, no less significant, are of a practical nature – face recognition algorithms cannot be assumed to operate on perfect data, but rather often on data that has already been subject to pre-processing errors (e.g localization and registration errors) This paper introduces a novel method for face recognition that is both trained and queried using only a single image per subject The key concept, motivated by abundant prior work on face appearance manifolds, is that of face part manifolds – it is shown that the appearance seen through a sliding window overlaid over an image of a face, traces a trajectory over a 2D manifold embedded in the image space We present a theoretical argument for the use of this representation and demonstrate how it can be effectively exploited in the single image based recognition It is shown that while inheriting the advantages of local feature methods, it also implicitly captures the geometric relationship between discriminative facial features and is naturally robust to face localization errors Our theoretical arguments are verified in an experimental evaluation on the Yale Face Database.