Face Recognition Attendance Monitoring System

Abstract

As we know attendance is the crucial part in colleges, schools, corporate offices etc. Attendance plays an important role for keeping track of student’s record. Mostly attendance is taken manually in most of the colleges and schools. The manual attendance process involves taking attendance by calling the name or by signing the attendance sheet. This manual process is very time consuming and it is difficult to maintain the attendance record. Due to this drawback of manual attendance system, there is a need of automated attendance system. Here facial recognition plays an important part for automated attendance system. This suggested study focuses on face detection algorithm to provide automated attendance system. This proposed system uses Histogram of Oriented Gradients (HOG) and Deep Convolutional Neural Network for face detection and face recognition respectively to build automated attendance system.

Introduction

I. INTRODUCTION

Today Recording someone’s presence through their attendance is crucial for every organization. The checking of attendance correlates with the academic performance in educational institute. Being present in an organization is evidence that a person is honouring their commitment to that agency or organization. In an institution where more than 100 students are studying in a class, it becomes very difficult to maintain the attendance record of such a large number of students. since the conventional method of monitoring class attendance requires a considerable amount of time and is vulnerable to errors and proxy attendances. To be able to accelerate and give time efficiency in this digital age, a change from this lack is necessary. We can effectively implement this face recognition-based system to mark the attendance by recognizing the faces of class students. Face recognition allows us to keep track of attendance for every person present in a company. Regular attendance has a significant role in quality monitoring and performance assessment in current academia. Numerous techniques, including Machine Learning and Deep Learning, are used in this face recognition to analyse and capture photos of people's faces. Persons in real time images can be detected and identified using many facial recognition systems and also facial recognition is a Biometric method. The presented approach focuses on recognition of students faces by comparing the student faces from the database, and marking the attendance of the student whose face is recognized. An email will be sent to the parents of the absent students. Here HOG is used for finding the faces and a deep learning model is used to recognize the face.

II. LITERATURE REVIEW

1. Attendance Management System using Facial Recognition [1]: This system focuses on automating attendance system by using HOG and neural network. It consists of four steps as finding all faces, posing and working faces, encoding faces and finding the person’s name from the encodings.
2. Smart Attendance System using Dlib Pre-Trained Neural Network [5]: This system solves the problem of face recognition using HOG and SVM classifier. The main task of the system is to recognise the users face and automatically mark the time of recognition directly in the google sheet. The accuracy of the system is 95.23%.
3. Real Time Face Recognition Based Attendance System for University Classroom [3]: Presented system not only mark the attendance but make an excel sheet to keep the record safe. First an HD 1080p camera captures the face images and then after noise reduction, HOG technique used to detect facial features. This system claims an accuracy of 77.38% for face recognition.
4. Non-Contact Temperature Detection, Face Mask Detection, and Attendance Updation System using Facial Recognition Technique [4]: To Mark the attendance of the individual employees swiping cards or biometrics are used. This can be easy medium for exchange of infection. This system dispenses sanitizer on arrival and also uses HOG algorithm for face recognition and updates the employee attendance. This system has an accuracy of 96.67%.
5. Design of an E-Attendance Checker through Facial Recognition using Histogram of Oriented Gradients with Support Vector Machine [2]: E-attendance checker using a facial recognition system was implemented. This system uses HOG and Support Vector Machine(SVM) for face recognition and detection. This system has an accuracy of 95.65% and can detect and recognize up to 37 students.
6. An approach towards development of automated attendance system using face detection and recognition [6]:  In this system software integrates face detection, image processing and face recognition approach to come up with a consolidated attendance system. The HOG approach is employed for the face detection mechanism and LBPH method is chosen for face recognition. This system has an accuracy of 91.75%.
7. Class attendance management system using face recognition [7]: The system is based on Raspberry Pi that runs Raspbian (Linux) Operating System installed on micro SD card. By facing the camera, the camera will capture the image then pass it to the Raspberry Pi which is programmed to handle the face recognition by implementing the Local Binary Patterns algorithm LBPs.  The system has 95% accuracy with the dataset of 11 person images.
8. A smart technique for attendance system to recognize faces through parallelism [9]: In this system first classroom image has been in use and after that image is kept in data record. For the images that are stored in the database it applies system algorithm which includes steps such as, histogram classification, noise removal, face detection and face recognition methods. So by using these steps it detect the faces and then compare it with the database. The attendance gets marked automatically if the system recognizes the faces.
9. Automated Attendance System using Machine Learning Approach [8]: This system uses Viola-Jones and HOG features along with SVM classifier are used to acquire the desired results. Various real time scenarios need to be considered such as scaling, illumination, occlusions and pose. The problem of redundancy in manual records and keeping attendance is solved by this system. Quantitative analysis is done on the basis of PSNR values.
10. Automatic Attendance Management System Using Face Detection [10]: In this method the camera is fixed in the classroom and it will capture the image, the faces are detected and then it is recognized with the database and finally the attendance is marked. If the attendance is marked as absent the message about the student's absent is send to their parents. This system uses eigen faces for facial recognition.

III. METHODOLOGY

This section deals with the steps required for face detection and face recognition. The work includes Six phases: Creating database and its training, Face Detection, Facial landmarks estimation, Encoding Faces, Finding person’s name from the encodings and Sending attendance record to parent.

1) Creating database and its training: In this first step the facial images of the students are stored in the database. Based on this facial images, features are extracted using a deep convolutional neural network model.

2) Face Detection: For detecting the faces from the images histogram of oriented gradients(HOG) approach is used. This approach deals with four steps:

a. Image Pre-processing: In this step, the image is bring down into width to height ratio of 1:2. The image size should be of 64x128. The image is then divided into 4x4 or 8x8 cells.

b. Calculating Gradients: In this step gradient values are calculated for both x and y direction. Gradient magnitude and gradient orientation is calculated for each pixel.

Gradient Magnitude =  √[(Gx)2+(Gy)2]

Gradient Orientation = tan-1(Gx / Gy)

c. Building Histogram: In this step a histogram is built using the values of gradient magnitude and gradient orientation. This step outputs a 1x9 matrix.

d. Normalization: In this step four cells of 4x4 or 8x8 are grouped together getting a 1x36 matrix. After that the matrix is normalized.

Conclusion

The proposed system helps to develop automated attendance system using facial recognition. In this mainly 3 steps are used for the work Histogram of Oriented Gradients(HOG), Facial Landmark and Deep Convolutional Neural Network. HOG approach is used for detecting the faces and a deep neural network is trained to extract 128 face encodings. Then these encodings are compared using Euclidean distance. If the face is matched the student is marked as present and at last the student faces that are not matched are marked as absent. An email is sent to the parents of absent students. This system will reduce the time that is required to take attendance in the college, schools, etc. The deep learning model we have used is the ResNet34 model which has an accuracy of 99.38% on LFW dataset. Thus we provide a system which is time efficient, simple and easy to use.

References

[1] Sarath Krishnan P, et al. “Attendance Management System Using Facial Recognition” IEEE 2022. [2] Nivedhitha DP, et al. “Smart Attendance System using Dlib Pre-Trained Neural Network ” IEEE 2023. [3] Hajrah Sultan, et al. “Real Time Face Recognition Based Attendance System For University Classroom” IEEE 2022. [4] Prajwal C Hegade, et al. “Non-Contact Temperature Detection, Face Mask Detection, and Attendance Updation System using Facial Recognition TechniqueClass Attendance System Using Viola-Jones Algorithm and Principal Component Analysis” IEEE 2021. [5] Allan Jason C, et al. “Design of an E-Attendance Checker through Facial Recognition using Histogram of Oriented Gradients with Support Vector Machine” IEEE 2020 [6] Sayan Seal, et al. “An approach towards development of automated attendance system using face detection and recognition” IEEE 2020. [7] Salim, Omar Abdul Rhman, Rashidah Funke Olanrewaju, and Wasiu Adebayo Balogun. \"Class attendance management system using face recognition.\" IEEE 2018. [8] B Prabhavathi et al. “A smart technique for attendance system” to recognize faces through parallelism IOP Conference Series: Materials Science and Engineering, 2017. [9] Rathod, Hemantkumar, et al. \"Automated attendance system using machine learning approach.\" International Conference on Nascent Technologies in Engineering IEEE, 2017. [10] E.Varadharajan et al. “Automatic Attendance Management System Using Face Detection” Online International Conference on Green Engineering and Technologies (IC-GET) 2016.

Copyright

Copyright © 2023 Om Dagade, Tejas Janjire, Sai Jagdale, Sanika Jawanjal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.