🤖 AI Summary
Current facial emotion recognition systems exhibit significant limitations in cross-dataset generalization, handling class imbalance, and distinguishing subtle emotional states—such as disgust versus anger. This study presents the first systematic evaluation of three state-of-the-art deep learning models across three large-scale, diverse datasets, revealing a substantial performance drop when models are tested on unseen data. The work further investigates how inter-dataset label inconsistencies and variations in annotation difficulty critically undermine model robustness. By identifying these key weaknesses in existing approaches, the study provides empirical evidence and clear directions for future research aimed at improving cross-domain generalization and fine-grained emotion recognition.
📝 Abstract
Emotion detection from faces is one of the machine learning problems needed for human-computer interaction. The variety of methods used is enormous, which motivated an in-depth review of articles and scientific studies. Three of the most interesting and best solutions are selected, followed by the selection of three datasets that stood out for the diversity and number of images in them. The selected neural networks are trained, and then a series of experiments are performed to compare their performance, including testing on different datasets than a model was trained on. This reveals weaknesses in existing solutions, including differences between datasets, unequal levels of difficulty in recognizing certain emotions and the challenges in differentiating between closely related emotions.