Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Human Recognition from Video
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
Adaptive Color-Image Embeddings for Database Navigation
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
The Earth Mover's Distance under Transformation Sets
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Journal of Cognitive Neuroscience
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
Many-to-many graph matching via metric embedding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Video-based face recognition: state of the art
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Robust gait recognition via discriminative set matching
Journal of Visual Communication and Image Representation
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In this paper, we present a novel approach of using Earth Mover's Distance for video-based face recognition. General methods can be classified into sequential approach and batch approach. Batch approach is to compute a similarity function between two videos. There are two classical batch methods. The one is to compute the angle between subspaces, and the other is to find K-L divergence between probabilistic models. This paper considers a most straightforward method of using distance for matching. We propose a metric based on an average Euclidean distance between two videos as the classifier. This metric makes use of Earth Mover's Distance (EMD) as the underlying similarity measurement between two distributions of face images. To make the algorithm more effective, dimensionality reduction is needed. Fisher's Linear Discriminant analysis (FLDA) is used for linear transformation and making each class more separable. The set of features is then compressed with a signature, which is composed of numbers of points and their corresponding weights. During matching, the distance between two signatures is computed by EMD. Experimental results demonstrate the efficiency of EMD for video-based face recognition.