The nature of statistical learning theory
The nature of statistical learning theory
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Open Set Face Recognition Using Transduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
A Comparative Study of Methods for Transductive Transfer Learning
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Proceedings of the 25th international conference on Machine learning
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Wikipedia for Co-clustering Based Cross-Domain Text Classification
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Toward Practical Smile Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Probabilistic Framework for Spontaneous Facial Action Modeling and Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bregman Divergence-Based Regularization for Transfer Subspace Learning
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Automatically Detecting Pain in Video Through Facial Action Units
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Domain adaptation for object recognition: An unsupervised approach
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Facing reality: an industrial view on large scale use of facial expression analysis
Proceedings of the 2013 on Emotion recognition in the wild challenge and workshop
Transfer learning with one-class data
Pattern Recognition Letters
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A key assumption of traditional machine learning approach is that the test data are draw from the same distribution as the training data. However, this assumption does not hold in many real-world scenarios. For example, in facial expression recognition, the appearance of an expression may vary significantly for different people. As a result, previous work has shown that learning from adequate person-specific data can improve the expression recognition performance over the one from generic data. However, person-specific data is typically very sparse in real-world applications due to the difficulties of data collection and labeling, and learning from sparse data may suffer from serious over-fitting. In this paper, we propose to learn a person-specific model through transfer learning. By transferring the informative knowledge from other people, it allows us to learn an accurate model for a new subject with only a small amount of person-specific data. We conduct extensive experiments to compare different person-specific models for facial expression and action unit (AU) recognition, and show that transfer learning significantly improves the recognition performance with a small amount of training data.