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Related Experiment Video

Updated: Jun 16, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Automated emotion recognition via video-based semantic embeddings.

Hannes Diemerling^1,2,3, Patricia Kulla⁴, Joachim Kruse⁴

¹Faculty of Health, Health and Medical University, Erfurt, Germany.

Frontiers in Digital Health

|June 15, 2026

Summary

This summary is machine-generated.

This study introduces AFFECT, an open-source pipeline for analyzing spontaneous emotional expressions. It accurately maps facial features to continuous emotion embeddings, enhancing automated emotion recognition systems.

Keywords:

automated facial expression analysis embedding emotion recognition (ER)machine learning transformer

Related Experiment Videos

Last Updated: Jun 16, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Area of Science:

Psychology
Computer Science
Artificial Intelligence

Background:

Automated emotion recognition often uses acted data, failing to capture spontaneous affect.
Existing models struggle with the nuance of real-world emotional expressions.

Purpose of the Study:

To develop a robust system for recognizing spontaneous facial emotion expressions.
To create an interpretable and open-source pipeline for affect analysis.

Main Methods:

Assembled a corpus of authentic facial emotion expressions from psychotherapy sessions.
Used a German Sentence-BERT model to create semantic embeddings of free-text annotations.
Trained Transformer, BILSTM, and DNN models to map facial landmarks to emotion embeddings.

Main Results:

Leave-one-out cross-validation showed model predictions closely matched human annotations (mean z-score 1.97).
External evaluation confirmed strong recognition of joy, sadness, and fear on acted datasets.
A back-translation mechanism enhanced interpretability, visualized with radar charts.

Conclusions:

The AFFECT pipeline offers accurate and interpretable analysis of spontaneous emotional expressions.
This approach advances automated emotion recognition beyond acted datasets.
The open-source nature facilitates broader research in affective computing.