The field of study is currently a hotspot of research because it has increasing applications in several domains, such as psychology, sociology, health science, transportation, gaming, communication, security, and business.
The overall performance is outstanding.įacial expression recognition (FER) is the automatic detection of the emotional state of a human face using computer-based technology. The proposed technique is evaluated using the Bosphorus, BU-3DFE, MMI, CK + , and BP4D-Spontaneous facial expression databases. The ensemble algorithm (Ada-AdaSVM) is then used for feature selection and classification. 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 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). However, FER is plagued with several challenges, the most serious of which is its poor prediction accuracy in severe head poses. Owing to its non-intrusiveness, it is considered a useful technology for combating crime. Facial expression recognition (FER) has numerous applications in computer security, neuroscience, psychology, and engineering.
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