Facial Emotion Recognition Based on Improved ResNet50 Using Hybrid Pooling and Adaptive Leaky ReLU
DOI:
https://doi.org/10.32628/IJSRSET251290Keywords:
deep learning, ResNet50, Facial Emotion Recognition, ReLUAbstract
Given the broad applications of facial emotion recognition technology, it has gained significant attention in recent years, spanning many fields, including human-computer interaction, healthcare, market research, and economic requirements. There are several types of emotions in humans, including anger, disgust, fear, happiness, neutrality, sadness, and surprise. Through these expressions, Methods can be implemented Facial emotion recognition. The ResNet50 improvement proposed aims to improve the representation and classification of facial expressions. In this study, we improve Resnet50 in two important areas to improve its efficiency. The first is to replace global average pooling with hybrid pooling and use a learnable α weight. The second is to improve Adaptive Leaky ReLU using a learnable parameter during training. In order to verify the validity of the proposed improvement, we used the well-known FER2013 and CK+ datasets and the proposed method showed an accuracy of 96.6% and 96.32%, respectively.
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