AI Based Facial Emotion Recognition

-20%

AI Based Facial Emotion Recognition

0 Orders 0 Wish listed

₹4,999.00

Qty

Total price:

₹4,999.00

Overview
Reviews

Detail Description

1.Abstract

Facial emotion recognition is an important application of artificial intelligence and computer vision that enables systems to identify human emotions through facial expressions. This project presents an AI-Based Facial Emotion Recognition System using YOLOv9, designed to detect human faces in real-time and classify their emotional states from live video streams.

The main objective of this system is to improve safety, surveillance, and behavioral analysis in real-world scenarios. Recognizing emotions such as anger, sadness, happiness, and surprise can help in identifying abnormal or critical situations. For example, in safety applications, detecting aggressive or distressed emotions can help trigger alerts and prevent potential incidents. In retail environments, analyzing customer emotions towards products can provide insights into customer preferences and improve decision-making.

The proposed system uses YOLOv9, a deep learning-based object detection model, to accurately detect faces in video frames. After detecting the face, a trained model is used to classify the facial expressions into predefined categories such as happy, sad, angry, and surprised. The system is trained using a custom dataset, which improves its performance in real-time conditions.

The implementation is carried out using Python and computer vision techniques, enabling efficient real-time processing. Additionally, the system is integrated with a mobile application that sends notifications whenever abnormal emotional activity is detected. This feature enhances the usability of the system for monitoring and safety purposes.

Although the system performs effectively, certain challenges such as lighting conditions, occlusions, and variations in facial expressions may affect accuracy. Despite these limitations, the project demonstrates the effectiveness of combining deep learning and computer vision techniques to build intelligent systems capable of understanding human emotions.

2. Objectives

To detect human faces in real time using YOLOv9.
To classify facial emotions such as happy, sad, angry, and surprised.
To identify abnormal emotional patterns for safety applications.
To send notifications to a mobile app when critical emotions are detected.
To build a real-time video streaming and monitoring system.
To apply emotion recognition in domains like safety and retail analytics.

3.Existing System

• In traditional surveillance and monitoring systems, facial emotion recognition is not integrated, and most systems only focus on basic face detection or manual observation.

• Existing systems rely on human monitoring to analyze facial expressions, which is time-consuming and prone to human error.

• Some basic machine learning models are used for emotion detection, but they often lack real-time performance and accuracy in complex environments.

• Most existing systems are not capable of sending real-time alerts or notifications when abnormal emotional behavior is detected.

4.Proposed System

• Develop a real-time facial emotion recognition system using YOLOv9 for accurate face detection and emotion classification.

• Implement deep learning techniques to classify facial expressions into categories such as happy, sad, angry, and surprised.

• Design a system that works efficiently on live video streams for continuous monitoring.

• Integrate a mobile application to send instant alerts when abnormal emotional behavior is detected.

• Utilize a custom dataset (Roboflow) to improve accuracy and adaptability in real-world scenarios.

5. Implementation Procedure

Module 1: Dataset Collection

Collect facial images with different emotions.
Use datasets from sources like Roboflow.
Label emotions (happy, sad, angry, surprised).

Module 2: Data Preprocessing & Visualization

Clean and preprocess images.
Visualize dataset distribution.
Perform data augmentation if needed.

Module 3: Model Training

Use YOLOv9 architecture for face detection.
Train emotion classification model on labeled dataset.
Optimize model for accuracy.

Module 4: Model Inference

Capture live video using webcam.
Detect faces and classify emotions in real time.
Display results on screen.

Module 5: Mobile App Integration

Connect system to mobile application.
Send notifications for abnormal emotions (e.g., anger, distress).

Module 6: Live Streaming

Stream real-time video to mobile app.
Display detected emotions and alerts.

6.Software Requirements

Operating System : Windows 10 / Linux

Programming Language : Python

Front End : HTML, CSS (for interface if used)

Back End : Python

Framework : PyTorch, OpenCV, YOLOv9

Database : Optional (for logs or alerts)

7.Hardware Requirements

Processor : Intel i3 or higher (i5 recommended)

RAM : 4 GB minimum (8 GB recommended)

Hard Disk : 500 GB

GPU : NVIDIA GPU (recommended for training and inference)

8. Advantages of the Project

Real-time emotion detection and analysis.
Enhances safety and surveillance systems.
Useful in retail for customer behavior analysis.
Automated alert system for critical situations.
Scalable and adaptable to multiple domains.
Non-intrusive monitoring system.

No review given yet!

Fast Delivery all across the country

Safe Payment

7 Days Return Policy

100% Authentic Products

Shopping cart