Facial Biometrics-Based Attendance Tracking System

Abstract

Facial attendance using face recognition and detection technology is a modern method of recording attendance based on facial features. This method is commonly used in automated attendance management systems and is efficient in tracking employee or student attendance without physical interaction. Face recognition has numerous applications such as security, surveillance, and human-computer interaction. This research aims to compare the performance of two popular face recognition techniques: HOG and KNN. The HOG algorithm extracts feature from an image using pixel intensity gradients while the KNN algorithm matches a test image with the most similar image in the training dataset. The study was conducted using the Labeled Faces in the Wild dataset available at https://vis-www.cs.umass.edu/lfw/. The results of the investigation show that the KNN algorithm outperforms the HOG algorithm in terms of accuracy. This research provides valuable insights into the effectiveness of different face recognition algorithms, helping researchers and developers choose the most suitable algorithm for their specific requirements.