Overview
At its core, the project employs advanced face recognition algorithms to identify individuals from images captured through cameras or uploaded files. The system is capable of recognizing faces with high accuracy, even in diverse lighting conditions and varying angles. Upon successful identification, the attendance of the recognized individuals is automatically recorded, eliminating the need for manual marking or sign-in sheets.
Project Keywords
Artificial Intelligence
Face Recognition
Attendance System
Automation
Deep Learning
Image Processing
Python
TensorFlow
PyTorch
Github
https://github.com/MahmoudDaasan/Attendance-System-Using-Artificial-Intelligence
Libraries
face_recognition: Core library for facial recognition tasks.PIL(Python Imaging Library),Image,ImageDraw: For image processing and drawing.pathlib.Path: Used for file and directory path operations.pickle: For serializing and deserializing Python object structures.
Challenges
Ensuring accurate face recognition across different lighting conditions and angles.
Managing large amounts of training and validation data efficiently.
Handling errors and exceptions during the face detection and recognition process.
Customizing the system to meet specific requirements of different settings or institutions.
Ensuring data privacy and security when dealing with facial images and attendance records.
Solutions
Utilizing advanced face recognition algorithms and training the model on diverse datasets to enhance robustness.
Implementing a structured directory system (
training/andvalidation/) for organizing and accessing data easily.Incorporating error-handling mechanisms within the code and providing informative error messages to guide users.
Allowing customization options such as
DEFAULT_ENCODINGS_PATH,BOUNDING_BOX_COLOR, andTEXT_COLORto adapt the system according to user preferences.Implementing encryption techniques and adhering to data protection regulations to safeguard sensitive information.
