Emotion-Aware AI Assistant for Intelligent Human–Computer Interaction
DOI:
https://doi.org/10.15662/IJEETR.2026.0802370Keywords:
Emotion Detection, Facial Expression Recognition, Convolutional Neural Network (CNN), Natural Language Processing (NLP), Emotion-Aware Chatbot, Computer Vision, Human–Computer Interaction, Text-to-Speech (TTS), Deep Learning, Real-Time Emotion Recognition.Abstract
Emotion-aware conversational systems are gaining importance in improving human–computer interaction. Traditional chatbots respond only to textual input and lack the ability to understand the user's emotional state. This limitation reduces the effectiveness of communication, especially in sensitive scenarios where empathy is required.
This project proposes an Emotion Detection with Chatbot system that identifies human emotions from facial expressions and generates appropriate conversational responses. The system captures real-time images using a webcam and detects faces using computer vision techniques. A pre-trained Convolutional Neural Network (CNN) model is used to classify emotions such as happy, sad, angry, and neutral. Based on the detected emotion, the chatbot generates context-aware responses using Natural Language Processing.
The integration of emotion recognition and conversational AI enables the system to provide more personalized and intelligent interactions. Experimental observations show that the system performs effectively under normal lighting conditions and produces relevant responses in real time. This approach can be applied in education, healthcare, and interactive assistance systems.
References
1) The work by Paul Ekman and Wallace V. Friesen introduced the Facial Action Coding System (FACS). This system categorizes facial movements into different action units based on muscle activity. It became a foundation for modern facial expression analysis. In emotion detection projects, FACS helps researchers understand how facial expressions relate to emotions such as happiness, sadness, and anger. This reference supports the concept of recognizing emotions from facial features.
2) This research by Ian Goodfellow and colleagues discusses challenges in representation learning using deep neural networks. The paper highlights how deep learning models can automatically extract meaningful features from images. This is important for emotion recognition because CNN models learn facial patterns without manual feature extraction. This reference supports the use of deep learning techniques in emotion detection systems.
3) The study by Takeo Kanade and team introduced a facial expression database used for training emotion recognition models. Such datasets contain labeled facial images representing different emotions. These datasets help researchers train and evaluate emotion detection algorithms. This reference supports the use of facial expression datasets like FER-2013 in the proposed project.
4) This reference describes the development of the OpenCV library by Gary Bradski. OpenCV provides tools for image processing and face detection. In this project, OpenCV is used to detect faces from webcam input before emotion classification. This reference supports the use of computer vision techniques in the system.
5) The research by Shiming Li and Weihong Deng focuses on deep facial expression recognition using modern deep learning methods. The paper discusses advanced CNN models that improve emotion classification accuracy. This reference supports the implementation of CNN-based emotion recognition in the proposed system.
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