Tag: Keras

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Detecting Impersonators in Examination Centres using AI

 

Detecting impersonators in examination halls is important to provide a better way of examination handling system which can help in reducing malpractices happening in examination centers.  According to the latest news reports, 56 JEE candidates who are potential impersonators were detected by a national testing agency. In order to solve this problem, an effective method is required with less manpower.

With the advancement of machine learning and AI technology, it is easy to solve this problem. In this project we are developing an AI system where images of students are collected with names and hall ticket numbers are pre-trained using the KDTree algorithm and the model is saved. Whenever a student enters the classroom, the student should look at the camera and enter class, after the given time or class is filled the student’s information will store in a  video file with the student’s name and hall ticket no. The video will have a user with a hall ticket no and name on each face. If the admin finds any unknown user tag on the face admin can recheck and trace impersonators. 

Problem statement:

Detecting impersonators in examination halls is important to provide a better way of examination handling system which can help in reducing malpractices happening in examination centers.  According to the latest news reports, 56 JEE candidates who are potential impersonators were detected by a national testing agency.

Existing system:

Information given in the hall ticket is used as verification to check if the student is the impersonator or not.  Manual security checks performed are not perfect and sometimes students can even change images from the hall ticket.    

Advantages:

Manual verification methods are used for checking personally for each student which is not possible to check each student personally.

Chances of changing images from hall tickets are possible which doesn’t have a verification method.

Proposed system:

  • In the proposed system initially, images of each student are collected and each dataset consists of 50 images of each student. These images are trained using kdtree algorithm using the image processing technique and the model is saved in the system this model can be used for automatic prediction of students in exam halls from live video or images. 

Advantages:

  • The student verification process is fast and accurate with the least effort. Reduces impersonator’s issue with live verification.
  • The time taken for prediction and processing is less and prediction is done automatically using a trained model.
  • A trained model can be used to track live video and automates the process of detecting students at exam centers and display them in the video.  

SOFTWARE REQUIREMENT: 

  •  Operating system:           Windows XP/7/10

  • Coding Language:           python

  • Development Kit             anaconda

  • Library:     Keras, OpenCV

  • Dataset:   any student’s dataset

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Movie Character Recognition From Video And Images Project

Live tracking of characters from movies is important for automating the process of classification for user-friendly information management systems like online platforms where characters in a movie can be seen before watching the movie. At present manual method is used which can be automated using this movie character classification method. The objective of this work is to collect a dataset of any movie characters and train a model which captures the facial features of all characters and the model is saved for prediction. 

For testing purposes, a real-time live video can be used to track characters. This application also works for images where users can give input as images of trained movie characters and get results with character names on the image as output. In this project for training dataset KDTree, the algorithm is used which takes images from a given folder and trains each image and saves the model into a dump file in the system. In the second stage using this trained model input image or input video is predicted with the model and the result is shown as a video or image.

Problem statement:

Classification of characters for each movie manually is a time taking process and the database should be managed.

Objective:

The objective of this project is to develop an automatic classification of characters after training from the dataset. If the one-time model is created it can be used for prediction at any time from images or video

Existing system:

In the existing system movie characters are managed in the database and which are used for displaying when required in this process database is the important to the time taken for processing is more.

Disadvantages:

  • The time taken for processing is more and the database should be managed and integrated with the required system whenever required.
  • This method includes the manual process of data collection and updating and deleting data. 

Proposed system:

In the proposed system initially, a dataset of respected move characters is collected and each dataset consists of 50 images. These images are trained using the KDDTree algorithm using the image processing technique and the model is saved in the system this model can be used for the automatic prediction of characters from live video or images.

Advantages:

  • The time taken for prediction and processing is less and prediction is done automatically using a trained model.
  • A trained model can be used to track live video and automates the process of detecting characters and displays on screens.

SOFTWARE REQUIREMENTS:

 Operating system:           Windows XP/7/10

  • Coding Language:  Python
  • Development Kit: Anaconda
  • Library:   TensorFlow, Keras, OpenCV
  • Dataset:  Any movie dataset