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Do Deepfakes Adequately Display Emotions? A Study on Deepfake Facial Emotion Expression.

Juan-Miguel López-Gil¹, Rosa Gil², Roberto García²

¹LSI Department, University of the Basque Country, Donostia-San Sebastián, Spain.

Computational Intelligence and Neuroscience

|October 28, 2022

Summary

This summary is machine-generated.

This study found that deepfake technology struggles to accurately replicate facial emotional expressions from original videos. Analyzing deepfakes reveals significant differences, suggesting improvements are needed for both deepfake creation and detection.

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Area of Science:

Computer Vision
Artificial Intelligence
Human-Computer Interaction

Background:

Advancements in AI enable creation of hyper-realistic deepfakes, often using face-swapping without consent.
Emotional expression is crucial for human communication, making its study in deepfakes relevant.

Purpose of the Study:

To conduct an in-depth study of facial emotional expression transfer in deepfakes.
To analyze the accuracy of emotional expression replication in face-swap deepfakes.

Main Methods:

Utilized a well-known face swap-based deepfake database.
Extracted individual frames (photograms) from original and deepfake videos.
Analyzed facial emotional expressions in both original and generated deepfake recordings.

Main Results:

Emotional expressions are not adequately transferred from original recordings to deepfakes.
Significant variability in emotion and performer expressiveness was detected between original and fake videos.

Conclusions:

Current deepfake generation methods show limitations in replicating authentic emotional expressions.
Performer emotion expressiveness is a critical factor to consider for improving deepfake generation and detection techniques.