aishmittal / solams

It is an Face Identification based automatic attendance management system which will be useful taking attendance during online lecture.

SOLAMS (Smart Online Lecture Attendance Management System)

SOLAMS is an Face Identification based automatic attendance management system which is useful for universities and educational websites which offers online lecture to students but wants to keep track of their attendance to ensure that students are taking lectures. It consists of two applications i.e. register user and main application.

New User Registration

Register application used to register the new user by taking some information about user and storing the information in a database and creating personid using Microsoft Face APIs. After the Face Dataset is generated using Opencv2. this generated face dataset is later uploaded to cloudinary and Microsoft cognitive service face APIs. after uploading dataset to microsoft cognitive service a model is build and trained.

Attendance during lecture

All registered users can login with their userid in main app. Login is face identification based so no password is required. After login user can select the course and lecture according to their choice and start lecture. Attendance is taken by capturing face at regular interval of time and verifying the detected personid with loggedin personid. Based on these regular attendance aggregate percent attendance is calculated according to total lecture duration and detection time interval. this aggregate percent attendance is then stored in database. Attendance for the lecture is marked as completed if percent attendance is greater than 60 percent.

Attendance Records

All users can view their attendance records by selecting the course in records tab in main app. It also gives the information of total course attendance.

Language used

Python 2.7

Python Packages Required

1. Opencv 2
2. PtQt 4
3. Sqlite3
4. PIL

APIs used

1. Microsoft Cognitive Services Face APIs (for building face identification model)
2. Cloudinary APIs (for storing dataset)
3. YouTube APIs (for getting video information of playlists)

Project Structure

   ├── dataset                               # Local Dataset storage path
   ├── tmp                                   # Local Path to store temprary files
   ├── main.py                               # Main app to view lectures and attendance records
   ├── register.py                           # Register app to register new users
   ├── MSFaceAPI.py                          # Mudule for interacting with MS FACE APIs
   ├── imageUpload.py                        # Module for uploading images to cloudinary
   ├── haarcascade_frontalface_default.xml   # Cascade Classifier for face recognition
   ├── cnf.ini                               # Configuration file to store API keys
   ├── solams.db                             # Sqlite3 database to store records
   ├── youtube-playlist.py                   # python script to store youtube video info in database
   └── README.md

Database used

sqlite3

Database Structure

• sudents

  • id -> userid
  • uname -> user name
  • fname -> first name
  • lname -> last name
  • dob -> date of birth
  • email -> user email id
  • gender -> gender
  • personid -> id of user in MS Face APIs
  • created at -> time stamp of user entry creation

• courses

  • id -> courseid
  • name -> course name
  • code -> university code of course
  • shortname -> shortname of the course
  • instructor -> instructor name
  • totallectures -> total no of lectures
  • url -> url of course playlist on youtube
  • description -> description of course
  • created -> timestamp

• lectures

  • id -> rowid
  • courseid -> froreign key from courses
  • lectureno -> lecture no
  • duration -> total duration of lecture in minutes
  • title -> lecture title
  • tilelbl -> lecture title label
  • videoid -> id of lecture video on youtube
  • description -> embed youtube url of lecture video
  • created -> timestamp

• attendance

  • id -> rowid
  • userid -> froreign key from students
  • courseid -> froreign key from courses
  • lectureno -> lecture no
  • percent_completed -> aggregate percent of lecture completion
  • start_time -> timestamp

Thanks

Feel free to post issues if you find any problem or contact me Aishwarya Mittal
©MIT License