Paper PublishingTransfer Learning for Recognizing Face in Disguise
Objective
In this paper, we aim to test some popular Convolutional Neural Network (CNN) Model Architecture to see which one is better to recognize the person face dataset in disguised. Here, we use the “Recognizing Disguised Faces” dataset to distinguish 75 classes of faces, and then try to train and test how accurate it can be recognized by the machine.
Abstract
Face recognition is a method in Machine Learning to recognize objects in the picture or video. Machine Learning is the technique or method in Computer Vision that can be used, so computers can understand one person’s face to another person contained in the image or video.
In this project, we propose about testing some popular Convolutional Neural Network (CNN) Model Architecture to see which one is better to recognize the person face dataset in disguised.
We use the “Recognizing Disguised Faces” dataset to distinguish 75 classes of faces, and then try to train and test how accurate it can be recognized by the machine, where it will be useful to anyone who needs to explore and develop an Architecture of Deep Learning.
Keywords: Face Recognition, Transfer Learning, Deep Learning, Machine Learning, CNN.
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,opencv,Matplotlib.
Learning Outcomes
- Scope of real time application scenarios.
- What is Tomcat server and how they can work.
- What type of technology versions?
- Use of Tkinter on UI Designs.
- Creating dataset.
- Storing data into CSV files.
- About pertained models.
- 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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