Advanced emotion analysis system that processes video, audio, and facial expressions to detect human emotions with 89% accuracy.
A comprehensive multimodal sentiment detection system that analyzes uploaded videos to identify human emotions by combining computer vision, audio processing, and natural language understanding.
Uses MediaPipe for face detection and landmark extraction, followed by a CNN trained on facial expression datasets. Processes 30 FPS with real-time performance.
Extracts MFCC features, spectrograms, and prosodic features using Librosa. Combines with speech-to-text for contextual analysis using BERT embeddings.
Late fusion approach combining predictions from visual, audio, and text modalities using weighted ensemble with learned attention mechanisms.