Development of Class Attendance System Using Face Recognition for Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia

Development of Class Attendance System Using Face Recognition for Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia

Pauline Ong (Universiti Tun Hussein Onn Malaysia, Malaysia), Tze Wei Chong (Universiti Tun Hussein Onn Malaysia, Malaysia) and Woon Kiow Lee (Universiti Tun Hussein Onn Malaysia, Malaysia)
DOI: 10.4018/978-1-7998-0182-5.ch001

Abstract

The traditional approach of student attendance monitoring system in Universiti Tun Hussein Onn Malaysia is slow and disruptive. As a solution, biometric verification based on face recognition for student attendance monitoring was presented. The face recognition system consisted of five main stages. Firstly, face images under various conditions were acquired. Next, face detection was performed using the Viola Jones algorithm to detect the face in the original image. The original image was minimized and transformed into grayscale for faster computation. Histogram techniques of oriented gradients was applied to extract the features from the grayscale images, followed by the principal component analysis (PCA) in dimension reduction stage. Face recognition, the last stage of the entire system, using support vector machine (SVM) as classifier. The development of a graphical user interface for student attendance monitoring was also involved. The highest face recognition accuracy of 62% was achieved. The obtained results are less promising which warrants further analysis and improvement.
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Introduction

Nowadays, biometric applications are widely applied across industries, institutions and government establishments. Biometric verification means that a person can be uniquely identified by evaluating one or more distinguishing biological traits (Zhang, 2013). Unique identifiers include fingerprints, face and hand geometry, retina and iris patterns, voice waves, DNA, and signatures. Every human has unique fingerprint, which is constructed by numerous ridges and valley on the surface of finger (Jain, Ross, & Prabhakar, 2004). Voice recognition is a technology that transforms the sound and words of human into electrical signals. These signals will then be converted into code design (Baumann, 1993). Iris recognition is one of the most accurate biometric verification techniques. The digital camera is used to take the impression of an iris, and then, the iris will be evaluated in the stored version (Saini & Rana, 2014). For hand geometry recognition, the shape, size of palm, and the lengths and width of the fingers are taken from the human hand based on a number of measurements (Jain, Flynn, & Ross, 2007).

Face recognition involves an analysis of facial features. Typically, it involves a computer system which determines or verifies an individual automatically from a digital image or video framework. The selected facial features from the image are then compared against the available features in facial database to identify the individual (Saini & Rana, 2014). Face recognition, in general, consists of three principal phases, specifically, face detection – detection of a human face from within the image phase for further scrutinization, feature extraction – extracting the distinguishable features from faces using the techniques of segmentation, image rendering and scaling, and face identification - applying the mathematical models or artificial intelligence methods to identify the face based on the features extracted from the facial area within an image.

Face recognition has been widely used in numerous applications, for instance, surveillance system (Zafar et al., 2019), photo album organization (Oh, Benenson, Fritz, & Schiele, 2018), identity verification in financial services (Szczuko, Czyżewski, Hoffmann, Bratoszewski, & Lech, 2019) and airport (Carlos-Roca, Torres, & Tena, 2018), and owner verification of mobile phone (Mahbub, Sarkar, & Chellappa, 2019). Using face recognition to monitor student attendance in universities is another potential application area to pursue. This is due to universities are using class attendance as a marker of student engagement for reasons including admission to the final examination. Most standard attendance monitoring methods, in fact, require trade-off between the effort spent to record the attendance and the aspect of accuracy (Joseph & Zacharia, 2013). For instance, lecturers take the student attendance at the expense of the available teaching time. This approach can be disruptive, imagine the case where the attendance list is passed around from attendee to attendee during an important part of the lecture (Alia, Tamimi, & Al-Allaf, 2013). Hence, utilization of biometric verification based on face recognition for student attendance seems to be a viable solution. Moreover, attendance monitoring using face recognition does not require any action from students. Student just simply let the camera or webcam to scan his/her face, and the attendance would be recorded automatically. Face recognition performs massive identification which usually other biometric systems could not do (Klokova, 2010). Taking the attendance based on face recognition helps to avoid attendance fraud on one hand, and reduces human mistake on another hand. Moreover, the paper-less attendance monitoring system contributes to a cleaner environment (Saini & Rana, 2014). The most important thing is that facial recognition technology can be easily programmed into real-time attendance monitoring system.

Key Terms in this Chapter

Viola Jones Algorithm: Viola-Jones algorithm is an object detection framework, with primary concern to solve the problem of face detection. Four stages are involved in the algorithm, namely, Haar feature selection, creating an integral image, Adaboost training, and cascading classifier.

Face Recognition: Face recognition involves an analysis of facial features, where in general, a computer system is utilized to identify an individual automatically from a digital image or video framework.

Support Vector Machine: Support vector machine is a supervised learning model with an associated learning algorithm that primarily used for classification.

Face Detection: Face detection attempts to identify a human face in digital images. Face detection is not the same as face recognition.

Biometric Verification: It’s an application which conducts measurement and identification based on physical or behavioral characteristics about a person (such as fingerprint or voice patterns), that can be applied to differentiate personal identities between individuals.

Principal Component Analysis: Principal component analysis is a statistical approach that transforms a set of observations of possibly correlated variables into a set of linearly uncorrelated principal components using orthogonal transformation.

Image Acquisition: In image processing, image acquisition is an action of retrieving image from an external source for further processing. It’s always the foundation step in the workflow since no process is available before obtaining an image.

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