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首页> 外文会议>International Conference on Automatic Face and Gesture Recognition >Support Vector Regression of Sparse Dictionary-based Features for View-Independent Action Unit Intensity Estimation
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Support Vector Regression of Sparse Dictionary-based Features for View-Independent Action Unit Intensity Estimation

机译：支持向量回归稀疏的基于字典的特征，以实现无关的动作单位强度估计

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摘要

In this paper, a robust system for viewindependent action unit intensity estimation is presented. Based on the theory of sparse coding, region-specific dictionaries are trained to approximate the characteristic of the individual action units. The system incorporates landmark detection, face alignment and contrast normalization to handle a large variety of different scenes. Coupled with head pose estimation, an ensemble of large margin classifiers is used to detect the individual action units. The experimental validation shows that our system is robust against pose variations and able to outperform the challenge baseline by more than 35%.
机译：本文介绍了一种用于查看依赖性动作单元强度估计的鲁棒系统。基于稀疏编码理论，训练区域特定词典以近似各个动作单元的特征。该系统包含地标检测，面部对齐和对比度标准化以处理大量不同的场景。耦合头姿势估计，用于检测各个动作单元的大型裕度分类器的集合。实验验证表明，我们的系统对姿态变化具有稳健，并且能够优于挑战基线超过35％。

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