📡Micro-Expression Detection

• User Uploads Video: Users upload video content for analysis. This could be an advertisement, a speech, or any other form of video content where emotional intent is critical.

• AI Micro-Expression Detection: Using advanced AI models, BitTruth detects facial micro-expressions and voice modulations that indicate emotions like nervousness, excitement, or deceit. For example:

Facial Micro-Expression: The AI detects subtle facial movements like a slight tightening of the mouth corner (indicating nervousness) or a brief raising of the eyebrows (indicating surprise or disbelief).

Voice Tone Analysis: The AI also assesses the speaker's voice for changes in pitch, pace, and tone that may indicate emotional states such as anxiety, joy, or aggression.

• Sincerity Score: The AI combines both the facial and vocal analysis to generate a sincerity score that indicates the authenticity of the emotions displayed. For example, if a speaker claims to be excited but their facial expression and voice tone suggest discomfort, the sincerity score might be low (e.g., 60%).

• Decentralized Validation: As with other features, the results of the analysis are validated by decentralized nodes, ensuring transparency and fairness in the process. This decentralized approach adds an extra layer of trust to the findings.

• Emotion Report: BitTruth generates an emotion report that highlights key emotional markers, including a breakdown of the sincerity score, specific micro-expressions, and voice tone shifts. Key frames showing significant emotional indicators are also annotated to provide a clear context.