Design and Evaluation of a Deep Learning Algorithm for Emotion Recognition
Objective
This paper attempts to discuss the application of emotion recognition where seven different emotions such as happy, sad, neutral, angry, surprise, fear and disgust are obtained using a Convolutional Neural Network.
Abstract
The study presents a comprehensive exploration of emotion recognition through the development and assessment of a sophisticated Deep Learning Algorithm. Employing Convolutional Neural Networks (CNNs), the algorithm effectively identifies and classifies seven distinct emotional states: sadness, happiness, neutrality, anger, surprise, fear, and disgust. The primary focus of this research is to optimize the algorithm's accuracy in accurately discerning these emotions from various input sources, such as images or audio data. Through rigorous evaluation, the algorithm demonstrates its robustness and adaptability in capturing the subtleties of human emotional expression. Such advancements in emotion recognition technology hold immense potential for applications in fields ranging from human-computer interaction to mental health monitoring, offering new ways to understand and engage with human emotions in an increasingly digitized world.
Keywords: Pre-processing, different types of Emotion images, Deep learning Technique, Convolutional Neural Network, Classification, Accuracy.
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
Related Projects
Paper Publishing
Request Call Back
Would you like to receive a free callback now?
Choose the best time for callback:
Leave your message and we will contact you as soon as possible
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, and not projects of or by IEEE.
