Analysis of Facial Sentiments: A Deep-learning Way
Objective
Here, our application introduces a Convolutional Neural Network (CNN) implemented architecture to detect the sentiment of a person using facial and visual clues. Such a system can be used to moderate content based on the sentiment of the person. This application is highly useful in modern world where we rely on video calls more and more.
Abstract
Human facial expressions are an integral means of displaying sentiments. Automatic analysis of these unspoken sentiments has been an interesting and challenging task in the domain of computer vision with its applications ranging across multiple domains including psychology, product marketing, process automation etc.
This task has been a difficult one as humans differ greatly in the manner of expressing their sentiments through expressions. Previously various machine learning techniques like Random forest, SVM etc. were used to predict the sentiment using converted images.
Deep learning has been instrumental in making breakthrough progress in many fields of research including computer vision. We implement a convolutional neural network (CNN) based model for facial sentiment detection. For training and testing purposes, the FER-2013 public dataset is utilized.
Keywords: Deep Learning, Facial Sentiment Analysis, Convolution Neural Network, Face Detection, Network Architecture.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE SPECIFICATIONS:
- Processor: I3/Intel
- Processor RAM: 4GB (min)
- Hard Disk: 128 GB
- Key Board: Standard Windows Keyboard
- Mouse: Two or Three Button Mouse
- Monitor: Any
SOFTWARE SPECIFICATIONS:
- Operating System: Windows 7+
- Server-side Script: Python 3.6+
- IDE: PyCharm
- Libraries Used: Pandas,TensorFlow,Matplotlib,Numpy.
- Frame Works:Flask.
Learning Outcomes
- Scope of real time application Scenarios
- How Internet Works.
- What type of technology versions?
- Use of HTML and CSS on UI Designs.
- Data Base Connections.
- How CNN works.
- Data Parsing Front-End to Back-End
- Need of PyCharm-IDE to Develop a web application
- Working Procedure
- Testing Techniques
- Error Correction mechanisms.
- How to run and deploy the applications.
- Introduction to basic technologies.
- How project works.
- Input and Output modules.
- How test the project based on user inputs and observe the output
- Project Development Skills:
- Problem analyzing skills.
- Problem solving skills.
- Creativity and imaginary skills.
- Programming skills.
- Deployment.
- Testing skills.
- Debugging skills.
- Project presentation skills.
- Thesis writing skills.
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