Attendance System using Face Recognition

Abstract

Abstract—In the current educational system, student’s frequent attendance in class is very important for performance evaluation and the quality control. Most institutions still use insecure, time-consuming traditional methods like calling people by the name or having them sign documents. Face recognition technology based on high-definition monitor video and other information technologies is used to solve the problem of the recognizing face for taking attendance. A facial recognition attendance system uses facial technology to automatically record attendance while identifying and verifying a person based on their facial features. This research focusing on a face recognition-based attendance system with getting a less false- positive rate using a threshold to confidence i.e., Euclidean distance value while detecting unknown persons and save their images. Compare to other Euclidean distance-based algorithms like Eigenfaces and Fisher faces, Local Binary Pattern Histogram (LBPH) algorithm is better. We used Haar cascade for face detection because of their robustness and LBPH algorithm for face recognition. It is robust against monotonic grayscale transformations. Scenarios such as face recognition rate, false- positive rate for that and false-positive rate with and without using a threshold in detecting unknown persons are considered to evaluate our system. We got face recognition rate of students is 77% and its false-positive rate is 28%. This system is recognizing students even when students are wearing glasses or grown a beard. Face Recognition of unknown persons is nearly 60% for both with and without applying threshold value. Its false-positive rate is 14% and 30% with and without applying threshold respectively. Keywords—Face recognition, face detection, machine learning, Haar Cascade Algorithm, Local Binary Pattens Histograms (LBPH).