Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Automated Facial Expression Recognition Based on FACS Action Units
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
Evaluation of Face Resolution for Expression Analysis
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
International Journal of Computer Vision
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
Discriminative Learning for Dynamic State Prediction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized isotonic conditional random fields
Machine Learning
Kernel conditional ordinal random fields for temporal segmentation of facial action units
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Conditional ordinal random fields for structured ordinal-valued label prediction
Data Mining and Knowledge Discovery
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We consider the task of labeling facial emotion intensities in videos, where the emotion intensities to be predicted have ordinal scales (e.g., low, medium, and high) that change in time. A significant challenge is that the rates of increase and decrease differ substantially across subjects. Moreover, the actual absolute differences of intensity values carry little information, with their relative order being more important. To solve the intensity prediction problem we propose a new dynamic ranking model that models the signal intensity at each time as a label on an ordinal scale and links the temporally proximal labels using dynamic smoothness constraints. This new model extends the successful static ordinal regression to a structured (dynamic) setting by using an analogy with Conditional Random Field (CRF) models in structured classification. We show that, although non-convex, the new model can be accurately learned using efficient gradient search. The predictions resulting from this dynamic ranking model show significant improvements over the regular CRFs, which fail to consider ordinal relationships between predicted labels. We also observe substantial improvements over static ranking models that do not exploit temporal dependencies of ordinal predictions. We demonstrate the benefits of our algorithm on the Cohn-Kanade dataset for the dynamic facial emotion intensity prediction problem and illustrate its performance in a controlled synthetic setting.