Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Exemplar-Based Face Recognition from Video
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
Robust Real-Time Face Detection
International Journal of Computer Vision
Video-Based Framework for Face Recognition in Video
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot 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
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Grassmann discriminant analysis: a unifying view on subspace-based learning
Proceedings of the 25th international conference on Machine learning
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Video face recognition with graph-based semi-supervised learning
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
An evaluation of video-to-video face verification
IEEE Transactions on Information Forensics and Security
From still image to video-based face recognition: an experimental analysis
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
Face recognition with the multiple constrained mutual subspace method
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Least squares quantization in PCM
IEEE Transactions on Information Theory
Bi-Modal Person Recognition on a Mobile Phone: Using Mobile Phone Data
ICMEW '12 Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops
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Grassmannian manifolds have been an effective way to represent image sets (video) which are mapped as data points on the manifold. Recognition can then be performed by applying the Discriminant Analysis (DA) on such manifolds. However, the local structure of the data points are not exploited in the DA. This paper proposes a modified Kernel Discriminant Analysis (KDA) approach on Grassmannian manifolds that utilizes the local structure of the data points on the manifold. The KDA exploits the local structure using between-class and within-class adjacency graphs that represent the between-class and within-class similarities, respectively. The maximum correlation from within-class and minimum correlation from between-class is utilized to define the connectivity between points in the graph thus exploiting the geometrical structure of the data. The discriminability is further improved by effective feature representation using LBP which can discriminate data across illumination, pose, and minor expressions. Effective recognition is performed by using only the cluster representatives extracted by clustering the frames of a video sequence. Experiments on several video datasets (Honda, MoBo, ChokePoint, NRC-IIT, and MOBIO) show that the proposed approach obtains better recognition rates, in comparison with the state-of-the-art approaches.