Main Article Content
According to a survey conducted by a health policy research group, mental health depression has had a negative effect on 53% of adults. Because of its numerous applications in artificial intelligence, detecting an emotion from a human face has become a demand. The aim of this study is to create a facial expression recognition system based on data augmentation and transfer learning. This method allows image data to be classified into seven basic emotions: rage, disgust, fear, happiness, neutrality, sadness, and surprise. Transfer learning with data augmentation achieves a higher level of precision (96.24 percent) and helps to address the shortcomings of existing models
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.