Depiction of the pre-processing adapted by the research workAn In-depth Facial Expression Study Using Multimodal Sentiment Analysis and Facial Emotion Recognition of Real-Time Video Using Neural Networks.
The project attempts to build a robust and efficient analytical study on human facial behavior by analyzing how the human face functions during a conversation and researching ways to find a correlation between what people say and the facial expressions they make when saying it. This research aims to help boost the accuracy of sentiment prediction by also analyzing the facial features along with the person’s speech transcriptions.
It has two parts:
Multimodal Sentiment Analysis(Video + Text)
Real Time Emotion Recognition
Niraj Yagnik
Head of Machine Learning
Passionate about AI, software engineering, and product development, with a focus on leveraging technology to democratize accessibility and create impactful solutions.