Facial expression recognition (FER) has numerous applications in computer security, neuroscience, psychology, and engineering. Owing to its non-intrusiveness, it is considered a useful technology for combating crime. However, FER is plagued with several challenges, the most serious of which is its poor prediction accuracy in severe head poses. The aim of this study, therefore, is to improve the recognition accuracy in severe head poses by proposing a robust 3D head-tracking algorithm based on an ellipsoidal model, advanced ensemble of AdaBoost, and saturated vector machine (SVM). The FER features are tracked from one frame to the next using the ellipsoidal tracking model, and the visible expressive facial key points are extracted using Gabor filters. The ensemble algorithm (Ada-AdaSVM) is then used for feature selection and classification. The proposed technique is evaluated using the Bosphorus, BU-3DFE, MMI, CK + , and BP4D-Spontaneous facial expression databases. The overall performance is outstanding.

中文翻译：

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强大的高位面部表情识别系统

面部表情识别 (FER) 在计算机安全、神经科学、心理学和工程学中有许多应用。由于其非侵入性，它被认为是打击犯罪的有用技术。然而，FER 面临着几个挑战，其中最严重的是其在严重头部姿势中的预测准确性差。因此，本研究的目的是通过提出一种基于椭圆体模型、高级 AdaBoost 集成和饱和向量机 (SVM) 的鲁棒 3D 头部跟踪算法来提高严重头部姿势的识别精度。使用椭球跟踪模型从一帧到下一帧跟踪 FER 特征，并使用 Gabor 滤波器提取可见的表情面部关键点。然后使用集成算法（Ada-AdaSVM）进行特征选择和分类。使用 Bosphorus、BU-3DFE、MMI、CK + 和 BP4D-自发面部表情数据库对所提出的技术进行了评估。整体表现非常出色。