Cross-Database Facial Expression Recognition using CNN with Attention Mechanism

Facial expression is one of the most effective and universal ways to express emotions and intentions. It reflects what a person is thinking or experiencing. Thus, the expression recognition is one of the key aspects of understanding non-verbal communication and interpreting emotions in social interactions. Some emotions are very confusing, and separating the features between them becomes difficult because they share the same feature space. For example, the distinction between fear, anger, and disgust is confusing. This work tried to improve the model’s class-wise performance to detect each class correctly. A distinct combination of deep-learning models is used to calculate the performance of the model, such as ResNet, XceptionNet, DenseNet, etc. The datasets like Real-world Affective Faces Database (RAF-DB), Japanese Female Facial Expression (JAFFE) & Facial Expression Recognition 2013 Plus (FER+) are used to evaluate the model’s performance. The proposed model achieved better results and overcame the previous work’s limitations. CDE’s performance on RAF-DB and FER+ evaluations was significantly better than the current SOTA methods, with an increase in accuracy of 5.18% and 3.98%, respectively.