Facial Keypoint Detection using a Modified Convolutional Neural Network with RESNET50

This study introduces an attentional convolutional network for facial expression recognition, highlighting the need of facial area focus for accurate emotion detection. By using this strategy, hybrid neural networks with ResNet50 architecture show promising f advances. Visualization accentuates face traits important for detecting emotions. Using transfer learning and fine-tuning, the hybrid ResNet50 architecture collects important face features to improve emotion recognition and generalization. Although Applications such as facial recognition and emotion tracking require accurate facial keypoint detection in order to function properly. The purpose of this research is to present an improved method that makes use of a modified convolutional neural network (CNN) that is constructed on the RESNET50 architecture. Enhancing RESNET50 with particular changes allows our method to increase both the extraction of features and the localization of keypoints respectively. Typical problems that are seen in conventional CNNs are addressed by the model that has been suggested. These problems include restricted feature representation and generalization concerns. A number of exhaustive studies have demonstrated that our method delivers a greater level of accuracy and robustness in facial keypoint detection when compared to the leading methods that are currently in use. With this new development, real applications now have access to a solution that is both more effective and more scalable.