Applying Transfer Testing to Identify Annotation Discrepancies in Facial Emotion Data Sets

The field of Artificial Intelligence (AI) has a significant impact on the way computers and humans interact. The topic of (facial) emotion recognition has gained a lot of attention in recent years. Majority of research literature focuses on improvement of algorithms and Machine Learning (ML) models for single data sets. Despite the impressive results achieved, the impact of the (training) data quality with its potential biases and annotation discrepancies is often neglected. Therefore, this paper demonstrates an approach to detect and evaluate annotation label discrepancies between three separate (facial) emotion recognition databases by Transfer Testing with three ML architectures. The findings indicate Transfer Testing to be a new promising method to detect inconsistencies in data annotations of emotional states, implying label bias and/or ambiguity. Therefore, Transfer Testing is a method to verify the transferability of trained ML models. Such research is the foundation for developing more accurate AI-based emotion recognition systems, which are also robust in real-life scenarios.