Affective Computing Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Affective Computing projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Affective Computing Final Year Projects

Analysis of Facial Expressions to Estimate the Level of Engagement in Online Lectures

This study developed a method to estimate how attentive students are during online lectures by analyzing their facial expressions. Researchers measured reaction time to sounds that were unrelated to the lecture and assumed slower reactions meant higher focus. They used a machine learning model to predict reaction times from facial movements. The results showed that facial expressions can reliably indicate students’ attention, even when they are not sleepy.
A Ranking Model for Evaluation of Conversation Partners Based on Rapport Levels

This project builds a system to rank conversation partners based on how well people get along. It uses data from both speech and text during interactions. Instead of predicting exact scores, it learns which partner is preferred over another. The model helps match people, like students and teachers, in online one-to-one sessions.
A Systematic Review of Facial Expression Detection Methods

This project studies how computers can recognize human emotions from facial expressions. It reviews many research studies that use deep learning techniques, especially convolutional neural networks. The work compares different methods and datasets to see which are most accurate. It helps understand which AI models work best for emotion detection.
Lightweight Deep Learning Framework for Speech Emotion Recognition

This project is about creating a system that can detect human emotions from speech. It uses a smart model that combines deep learning and simpler machine learning methods to work efficiently. The system is designed to run fast even on devices with limited resources. Tests on several speech datasets showed that it can recognize emotions like happy, sad, angry, and calm with very high accuracy.

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Affective Computing Project Synopsis & Presentation

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Affective Computing Final Year Projects with Source Code