Efficientfer: EfficientNetV2 Based Deep Learning Approach for Facial Expression Recognition

Abstract

Facial expression recognition (FER), aiming to classify human emotions automatically, is a significant problem in computer vision. Recent advancements in deep learning and computer vision have led to notable progress in FER. This work proposes an enhanced emotion recognition framework utilizing the FER-2013 dataset, augmented with additional training data for improved generalization performance. The EfficientNetv2 architecture is employed with transfer learning for robust and comprehensive feature extraction. The proposed method leverages attention mechanisms to capture critical facial details while mitigating the influence of irrelevant information. The model trained with approximately 23.8 million parameters surpassed the performance of existing methods by classifying with %82.47 accuracy rate on the FER-2013 dataset. These results indicate the potential applicability of the proposed approach to emotion recognition tasks.