Clinical Report: Video-Based Semantic Embeddings for Automated Recognition of Emotions

Overview

This study presents a novel automated emotion recognition system utilizing a large corpus of authentic facial expressions from psychotherapy sessions. The model demonstrates strong alignment with human annotations and effective recognition of key emotions such as joy, sadness, and fear.

Background

Accurate emotion recognition is crucial in clinical settings, particularly in psychotherapy, where it informs treatment decisions. Traditional methods often rely on acted datasets, which may not capture the complexity of spontaneous emotional expressions. This research aims to enhance emotion recognition through advanced modeling techniques that reflect real-world emotional dynamics.

Data Highlights

Leave-one-out cross-validation yielded a mean z-score of 1.97, indicating strong model performance. External evaluation against the RAVDESS dataset confirmed effective recognition of joy, sadness, and fear.

Key Findings

• The model was trained on a large corpus of authentic facial emotion expressions from psychotherapy sessions.
• Human annotations were embedded in a 768-dimensional semantic space using a fine-tuned German Sentence-BERT model.
• Transformer, BILSTM, and deep neural network architectures were employed to map facial landmark features to continuous emotion embeddings.
• A back-translation mechanism using cosine similarity was implemented for enhanced interpretability.
• The system, named AFFECT, is an open-source pipeline for analyzing emotional expressions in everyday video recordings.

Clinical Implications

The findings suggest that automated emotion recognition systems can provide valuable support in clinical settings by offering objective assessments of patients' emotional states. This may enhance the clinician's ability to tailor interventions based on real-time emotional feedback.

Conclusion

The development of AFFECT represents a significant advancement in automated emotion recognition, with potential applications in clinical practice and beyond.

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