Paper PublishingCovid-19-Preventions-Control-System and Unconstrained Face-Mask And Face-Hand Detection Framework
Objective
The main objective of this project is to identify a person is wearing a mask or not properly wearing a mask or face hand interaction. To achieve this process we implementing the deep learning techniques.
Abstract
The end of 2019 witnessed the outbreak of Coronavirus Disease 2019 (COVID-19), which has continued to be the cause of plight for millions of lives and businesses even in 2021. As the world recovers from the pandemic and plans to return to a state of normalcy, there is a wave of anxiety among all individuals, especially those who intend to resume in person activity. Studies have proved that wearing a face mask significantly reduces the risk of viral transmission as well as provides a sense of protection. However, it is not feasible to manually track the implementation of this policy. Technology holds the key here. So we are introducing a system based on deep learning that which can identify the person either wearing a mask properly or not. To implement the process we consider the dataset called MAFA-data which will be trained using Convolution neural network (CNN) along with computer vision.
INDEX TERMS: Deep Learning, Convolutional Neural Network, Face mask image dataset.
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, Numpy,Os,TensorFlow,Flask,Matplotlib.
Learning Outcomes
- Convolutional Neural Network.
- Deep Learning.
- Fine-tuning.
- Rice leaf diseases.
- Transfer learning.
- Importance of PyCharm IDE.
- Process of debugging a code.
- The problem with imbalanced dataset.
- Benefits of SMOTE technique.
- 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.
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.
