Facial expressions analysis for deep fake and genuine video recognition

Facial Expression analysis (FEA) is a process that involves recognition and understanding of human emotions based on facial cues. While FEA has potential applications in various field, this can also be misused, leading to the spread of misinformation through deepfake technology. This research aims to evaluate the effectiveness of facial expressions in distinguishing between deepfake and genuine videos, addressing the gap in how well FEA can identify manipulated contents. To address this issue, a research experiment was conducted to gain an insight into how people react towards deepfake and authentic contents. Respondents were shown videos and an analysis was conducted on participant’s facial expressions as well as assessing their knowledge of deepfake detection. A survey was designed to test their confidence with the level of deepfake and authentic video identification, trust, security, and attitude towards them. Facial expressions were analyzed using Noldus FaceReader 7 to detect and classify 7 facial expressions (such as happy, sad, neutral, angry, surprised, disgusted, and other). The study findings indicate that FaceReader analysis discerns a statistically significant difference in emotional responses between real and deepfake videos, while participants reported a higher percentage of neutrality (70% vs. 62.5%) in real videos compared to deepfakes.