Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition from Long-Term Observations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
A framework for 3d object recognition using the kernel constrained mutual subspace method
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Image set-based hand shape recognition using camera selection driven by multi-class AdaBoosting
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Set based discriminative ranking for recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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Face recognition using image-set or video sequence as input tends to be more robust since image-set or video sequence provides much more information than single snapshot about the variation in the appearance of the target subject. Usually the distribution of such image-set approximately resides in a low dimensional linear subspace and the distance between image-set pairs can be defined based on the concept of principal angles between the corresponding subspace bases. Inspired by the work of [4,14], this paper presents a robust framework for image-set based face recognition using boosted global and local principal angles. The original multi-class classification problem is firstly transformed into a binary classification task where the positive class is the principal angle based intra-class subspace “difference” and the negative one is the principal angle based inter-class subspace “difference”. The principal angles are computed not only globally for the whole pattern space but also locally for a set of partitioned sub-patterns. The discriminative power of each principal angle for the global and each local sub-pattern is explicitly exploited by learning a strong classifier in a boosting manner. Extensive experiments on real life data sets show that the proposed method outperforms previous state-of-the-art algorithms in terms of classification accuracy.