![]() ![]() As a result, researchers want to use the human visual mechanism (i.e., embed attention modules in neural networks to mimic human visual perception) to enable the neural network to ignore irrelevant information and focus more on the important information. Īlthough the previously mentioned methods have some effects on expression recognition, there are some limitations for example, eliminating interference factors may weaken some important facial features. To address the issue, a variety of facial expression recognition methods learn facial expression features by eliminating the interference caused by various interference factors such as posture, identity, and illumination, and have improved recognition performance for many public datasets collected in the laboratory or through various ways such as CK+, MMI, Oulu-CASIA, SFEW/AFEW, FERPlus, AffectNet, EmotioNet, and RAF-DB. Nonetheless, the accuracy of facial expression recognition in video sequences is still influenced by lighting, deflection, occlusion, and other objective factors affecting image quality. created a network that extends the well-known 2D Inception-ResNet module, which is followed by a long short-term memory (LSTM) that classifies the sequences using these temporal relationships. proposed a spatio-temporal model obtained from the dense low-level features of the video subsequently, the generalized flow model is learned and fitted from all low-level features. Because facial expression reflected in video sequences is a dynamic process, many studies now employ dynamic methods to learn face image features while incorporating face networks to extract temporal and spatial features of facial expression images. Among them, the mainstream methods of static facial expression recognition include traditional manual feature methods such as LBP and SIFT nevertheless, the aforementioned traditional methods have difficulty extracting powerful temporal features hidden in facial images by manual descriptors. A variety of novel methods have greatly improved the accuracy of facial expression recognition. Over the last 20 years, the field of computer vision has advanced rapidly, with facial expression recognition being a focal point due to its widespread application in human life such as human–computer interaction, virtual reality, intelligent course systems, and so on. Most studies relevant to neutral emotions are based on the six basic emotions. According to Ekman’s six basic cross-cultural emotions theory, facial expressions can be divided into six categories (i.e., anger, disgust, fear, happiness, sadness, and surprise). Human facial expression is one of the most natural and universal physiological signals by which humans can convey their feelings and behavioral trends. ![]()
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