Camera Based Attendance App

Overview:

This project focuses on the development of Camera Based Attendance App for Multiple Faces as my fullfillment of my Major Project.This innovative project aimed at transforming traditional attendance management systems by leveraging advanced facial recognition technology. The primary objective of this project is to develop an automated image-based attendance recording solution, that eliminates the need for manual attendance recording,reduces the potential for errors, and provides real-time data

Approach

• Model creation and training
• Developing a Mobile App
• Facilating Communication between the app and the model using an API

File structure:

Name	Type	Description
ML	Directory	Codefiles for the face detection and recognition model
MobileApp	Directory	Codefiles for development of the mobile app
Flask	Directory	Creation of API
attendanceAppTrial.apk	APK file	The final apk file for installation on android

Process:

• Capturing images for the dataset:

1. Navigate to the ML folder
2. • For using Haar-Cascade Classifier to extract faces for training:
     

   Run Haar_dataset_capture.py
   

   • For using MTCNN to extract faces for training:

   Run mtcnn_capture.py

3. The UI opens for the capture of the image files. The user needs to enter the name and roll number and then the camera automaticallly starts capturing the images for the dataset for that particular user.
4. The extracted face are saved for the next step of model training
   (Note: The facial data gets saved to Haar_Face_Dataset folder or MTCNN_Face_Dataset for Haar-Cascade or MTCNN respectively)

• Training the model using SVC

1. In the ML folder:

   Run the svm1.py file

   Notes:
   1. Both the training and testing code for the model is present in svm1.py.

   2. The default folder for training images is Haar_Face_Dataset but can be changed in the main() method by changing the variable train_data_folder.

   3. The test image is given by the variable test_image_path in the main() method.

2. The model gets saved as a .pkl file.

   Note: The default name for the .pkl file is FaceRecognition.pkl an can be changed by changing the variable model_filename in the train_face_recognizer() method

• Creating an API for the model

  The Flask directory contains the code for the creation of api for the model. This api shall help in the communication between the app and the model and thus as long as the api is running the app can provide results. This also makes changing or updating the model easier.

• Creating the android app.

  The mobile app is created using Flutter the code files of which are present in the MobileApp directory. The app is then exported as an apk file named attendanceAppTrial.apk which can be found in the root of this repo