Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
Development of a Face Recognition System on an Image Processing LSI Chip
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Random sampling LDA for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Person recognition using facial video information: A state of the art
Journal of Visual Languages and Computing
On-line learning of mutually orthogonal subspaces for face recognition by image sets
IEEE Transactions on Image Processing
An evaluation of video-to-video face verification
IEEE Transactions on Information Forensics and Security
Video-based face recognition: state of the art
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Face recognition technology and its real-world application
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Robust gait recognition via discriminative set matching
Journal of Visual Communication and Image Representation
Face recognition in videos: a graph based modified kernel discriminant analysis
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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In this paper, we propose a novel method named the Multiple Constrained Mutual Subspace Method which increases the accuracy of face recognition by introducing a framework provided by ensemble learning. In our method we represent the set of patterns as a low-dimensional subspace, and calculate the similarity between an input subspace and a reference subspace, representing learnt identity. To extract effective features for identification both subspaces are projected onto multiple constraint subspaces. For generating constraint subspaces we apply ensemble learning algorithms, i.e. Bagging and Boosting. Through experimental results we show the effectiveness of our method.