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首页> 外文期刊>Affective Computing, IEEE Transactions on >Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests
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Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests

机译：多视图成对条件随机林的动态姿态强大的面部表情识别

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摘要

Automatic facial expression classification (FER) from videos is a critical problem for the development of intelligent human-computer interaction systems. Still, it is a challenging problem that involves capturing high-dimensional spatio-temporal patterns describing the variation of one's appearance over time. Such representation undergoes great variability of the facial morphology and environmental factors as well as head pose variations. In this paper, we use Conditional Random Forests to capture low-level expression transition patterns. More specifically, heterogeneous derivative features (e.g., feature point movements or texture variations) are evaluated upon pairs of images. When testing on a video frame, pairs are created between this current frame and previous ones and predictions for each previous frame are used to draw trees from Pairwise Conditional Random Forests (PCRF) whose pairwise outputs are averaged over time to produce robust estimates. Moreover, PCRF collections can also be conditioned on head pose estimation for multi-view dynamic FER. As such, our approach appears as a natural extension of Random Forests for learning spatio-temporal patterns, potentially from multiple viewpoints. Experiments on popular datasets show that our method leads to significant improvements over standard Random Forests as well as state-of-the-art approaches on several scenarios, including a novel multi-view video corpus generated from a publicly available database.
机译：来自视频的自动面部表情分类（FER）是开发智能人机交互系统的关键问题。尽管如此，这是一个具有挑战性的问题，涉及捕获描述一个随时间外观变化的高维时空模式。这种代表经历了面部形态和环境因素以及头部姿势变化的巨大变化。在本文中，我们使用条件随机森林来捕获低级表达式转换模式。更具体地，在图像对时评估异构衍生特征（例如，特征点运动或纹理变化）。当在视频帧上测试时，在该当前帧之间创建对时，并且每个先前帧的预测用于绘制从成对条件随机林（PCRF）的树木，其成对输出随着时间的平均而平均，以产生鲁棒估计。此外，PCRF收集也可以在用于多视图动态FER的头部姿势估计上调节。因此，我们的方法表现为随机森林的自然延伸，用于学习时空模式，可能来自多个观点。流行数据集的实验表明，我们的方法导致标准随机林的显着改进以及若干方案的最先进的方法，包括从公共可用数据库生成的新型多视图视频语料库。

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