AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Facial Expression Recognition (FER) is an important component of affective computing and human-computer interaction, yet it is difficult to apply in practice because of the variations in illumination, pose variation, occlusions, low-resolution images, and skew in the classes. To overcome these issues, this paper introd...
A hybrid model which uses several ResNet50 mixed with VGG16 backbones to improve the local facial features with the global context for a robust FER solu-tion, and shows superior robustness to occlusions and pose, even under cross-dataset testing.
EmotionNet is a neural network that is used to recognize facial emotions in real-time and under dynamic and unconstrained conditions using videos. The suggested system combines both spatial features extraction and time modeling to ensure the capture of facial appearance and motion-based emotion features. A convoluted n...
Improvements suggest that adaptive centroid-aware local modeling, structure-aware ipsilateral dependency learning, and hierarchical dual-stream fusion are complementary and effective for robust MER under cross-dataset variation.
It is seen that the joint enhancement-recognition models can be more effective in restoring the semantically meaningful features when the light is low, and attention and transformer-based models are more efficient in coping with the occlusion through focusing on informative areas of the faces.
-Facial emotion recognition (FER) has emerged as a critical area of research in computer vision and affective computing, with applications spanning healthcare, human-computer interaction, security, and education. This paper presents a hybrid deep learning framework combining Convolutional Neural Networks (CNNs) and Lon...
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