Surface shape and curvature scales
Image and Vision Computing
The nature of statistical learning theory
The nature of statistical learning theory
Improving Regressors using Boosting Techniques
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An Expert System for Recognition of Facial Actions and their Intensity
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
3D Facial Expression Recognition Based on Primitive Surface Feature Distribution
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Experiments with AdaBoost.RT, an improved boosting scheme for regression
Neural Computation
Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Practical Smile Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time 2D+3D facial action and expression recognition
Pattern Recognition
A Dynamic Texture-Based Approach to Recognition of Facial Actions and Their Temporal Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
High-resolution, real-time 3D imaging with fringe analysis
Journal of Real-Time Image Processing
Facial expression recognition using 3D facial feature distances
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Editorial: 3D facial behaviour analysis and understanding
Image and Vision Computing
Proceedings of the 14th ACM international conference on Multimodal interaction
Why is facial expression analysis in the wild challenging?
Proceedings of the 2013 on Emotion recognition in the wild challenge and workshop
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Facial Action Coding System (FACS) is the de facto standard in the analysis of facial expressions. FACS describes expressions in terms of the configuration and strength of atomic units called Action Units: AUs. FACS defines 44 AUs and each AU intensity is defined on a nonlinear scale of five grades. There has been significant progress in the literature on the detection of AUs. However, the companion problem of estimating the AU strengths has not been much investigated. In this work we propose a novel AU intensity estimation scheme applied to 2D luminance and/or 3D surface geometry images. Our scheme is based on regression of selected image features. These features are either non-specific, that is, those inherited from the AU detection algorithm, or are specific in that they are selected for the sole purpose of intensity estimation. For thoroughness, various types of local 3D shape indicators have been considered, such as mean curvature, Gaussian curvature, shape index and curvedness, as well as their fusion. The feature selection from the initial plethora of Gabor moments is instrumented via a regression that optimizes the AU intensity predictions. Our AU intensity estimator is person-independent and when tested on 25 AUs that appear singly or in various combinations, it performs significantly better than the state-of-the-art method which is based on the margins of SVMs designed for AU detection. When evaluated comparatively, one can see that the 2D and 3D modalities have relative merits per upper face and lower face AUs, respectively, and that there is an overall improvement if 2D and 3D intensity estimations are used in fusion.