Facial Expression Image based Emotion Detection using Convolutional Neural Network
Objective
The goal is to create a model that can automatically recognize and classify human emotions from facial images into predefined categories such as happiness, sadness, anger, surprise, fear, and disgust.
Abstract
Facial Expression Image-based Emotion Detection using Convolutional Neural Networks (CNNs) involves a multi-step process. First, the input facial images are pre-processed to enhance features and reduce noise, optimizing them for subsequent analysis. Next, a CNN architecture is designed and trained to classify emotions based on the provided labeled dataset encompassing primary emotions (Anger, Disgust, Fear, Happy, Sad, Surprise, and Neutral). The CNN learns to extract intricate patterns and features from these images, enabling accurate emotion recognition. Evaluating the model's performance involves metrics such as accuracy, precision, recall, and F1-score, providing comprehensive insights into the model's ability to correctly classify emotions. Achieving high accuracy and balanced precision, recall, and F1-score demonstrates the effectiveness of the CNN in detecting emotions from facial expressions, paving the way for applications in various domains such as human-computer interaction, mental health assessment, and affective computing.
Keywords: convolutional neural network, emotion detection, facial images
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is an Image/pixel?
· About image formats
· Introduction to Image Processing
· How digital image is formed
· Importing the image via image acquisition tools
· Analyzing and manipulation of image.
· Phases of image processing:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
