This study delved into the realm of facial emotion recognition within virtual reality (VR) environments. Using a novel system with MobileNet V2, a lightweight convolutional neural network, we tested emotion detection on 15 university students. High recognition rates were observed for emotions like "Neutral", "Happiness", "Sadness", and "Surprise". However, the model struggled with 'Anger' and 'Fear', often confusing them with "neutral". These discrepancies might be attributed to overlapping facial indicators, limited training samples, and the precision of the devices used. Nonetheless, our research underscores the viability of using facial emotion recognition technology in VR and recommends model improvements, the adoption of advanced devices, and a more holistic approach to foster the future development of VR emotion recognition.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598841 | PMC |
| http://dx.doi.org/10.3389/fpsyg.2023.1280136 | DOI Listing |
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