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Towards a robust affect recognition: Automatic facial expression recognition in 3D faces

机译：迈向强大的情感识别：3D面部中的自动面部表情识别

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摘要

Facial expressions are a powerful tool that communicates a person's emotional state and subsequently his/her intentions. Compared to 2D face images, 3D face images offer more granular cues that are not available in the 2D images. However, one major setback of 3D faces is that they impose a higher dimensionality than 2D faces. In this paper, we attempt to address this problem by proposing a fully automatic 3D facial expression recognition model that tackles the high dimensionality problem in a twofold solution. First, we transform the 3D faces into the 2D plane using conformal mapping. Second, we propose a Differential Evolution (DE) based optimization algorithm to select the optimal facial feature set and the classifier parameters simultaneously. The optimal features are selected from a pool of Speed Up Robust Features (SURF) descriptors of all the prospective facial points. The proposed model yielded an average recognition accuracy of 79% using the Bosphorus database and 79.36% using the BU-3DFE database. In addition, we exploit the facial muscular movements to enhance the probability estimation (PE) of Support Vector Machine (SVM). Joint application of feature selection with the proposed enhanced PE (EPE) yielded an average recognition accuracy of 84% using the Bosphorus database and 85.81% using the BU-3DFE database, which is statistically significantly better (at p ＜ 0.01 and p ＜ 0.001, respectively) if compared to the individual exploit of the optimal features only.
机译：面部表情是传达人的情绪状态以及随后的意图的有力工具。与2D脸部图像相比，3D脸部图像提供了2D图像中不可用的更多颗粒提示。但是，3D面的一个主要挫折是它们比2D面具有更高的尺寸。在本文中，我们尝试通过提出一种全自动3D面部表情识别模型来解决此问题，该模型可通过双重解决方案解决高维问题。首先，我们使用共形映射将3D面转换为2D平面。其次，我们提出了一种基于差分进化（DE）的优化算法，以同时选择最佳面部特征集和分类器参数。从所有预期面部点的加速鲁棒特征（SURF）描述符池中选择最佳特征。提出的模型在Bosphorus数据库中的平均识别准确度为79％，在BU-3DFE数据库中的平均识别准确度为79.36％。此外，我们利用面部肌肉运动来增强支持向量机（SVM）的概率估计（PE）。使用Bosphorus数据库将特征选择与建议的增强型PE（EPE）结合使用时，平均识别准确率达到84％，而使用BU-3DFE数据库则达到85.81％，在统计学上要好得多（p ＜0.01和p ＜0.001，分别与仅使用最佳功能的情况进行比较。

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