Design and Development of IoT Based Student s Attendance System

Transcription

1 Design and Development of IoT Based Student s Attendance System Kunal Kishore 1 Ozwin Neel Lobo 2 Pranay Saraiwala 3 Prarthana R 4 ABSTRACT Traditional ways of attendance monitoring system is tedious, time-consuming and prone to human error. Hence there is a need for an efficient and cost effective system. In recent years, machine learning algorithms in the field of image processing have become an important part for the purpose of security and surveillance. The goal of this paper is to explore the feasibility of implementing IoT based face recognition system using the conventional face detection algorithm such as Haar Cascades in OpenCV, and face recognition is done using Eigen face approach and Principal Component Analysis(PCA) algorithm. Our proposed system uses a camera to detect the students present in the class and update the attendance-database accordingly. KEYWORDS Automated attendance system, PCA algorithm, Eigen face, Eigen vectors, Haar cascades, Face recognition, Face detection, Raspberry PI, OpenCv INTRODUCTION The traditional approach of monitoring the attendance of a class in a register is very tedious and timeconsuming, and also can be easily manipulated, destroyed or misplaced[4]. To avoid this problem of manual attendance system, different biometric techniques like fingerprints, iris recognition, smart card etc. have evolved and successfully used in all sized organisations [3,7]. But even these techniques are not practical for large scale applications and has a disadvantage of congestion and slow adoption. As the number of students enrolling to the universities increases every year, maintaining the attendance and records of thousands of students is one of the major concerns in the education sector. Hence there is a need for a feasible, cost effective system with less human intervention [1]. In recent years, image and video based face recognition has received extensive attention and is one of the most important topics of research in the field of image processing for people s identification. Literature survey statistics shows that research work in face recognition system is in its booming era, and in the past forty years, the research in this field has increased exponentially [2]. Face recognition technology finds numerous applications in the fields of automated surveillance, monitoring closed circuit television(cctv) to track missing persons and suspected terrorists, forensic applications, multimedia applications, face reconstructions. Also, Face recognition technology finds a major application in the field of education to efficiently automate and manage the attendance system. There are so many other methods of identification which can be more accurate than face recognition, still, it is gradually evolving in 506 Kunal Kishore, Ozwin Neel Lobo, Pranay Saraiwala, Prarthana R

2 the biometrics because of its non-invasive nature and because it is the primary method for person s identification. There are several challenges involved in traditional face recognition technology like varying intensity of light, facial expressions, pose variations, partial occlusions like presence of objects in between, change of hairstyle or beard, changes in facial features due to age, which impacts the face recognition performance. Therefore developing an efficient model of face detection and recognition is crucial as they are complex and has multidimensional views [1, 2]. The goal of this paper is to demonstrate how face recognition technique can be effectively utilized in the education sector for an effective automated attendance system to record the presence of an enrolled individual within the respective venue i.e., the classroom. The process of this face recognition system is divided into various steps, but the most important steps are detection and recognition of the face[4]. The framework is such that it uses face detection algorithm - Haar cascades and face recognition algorithm Eigen Face approach and PCA (Principal Component Analysis) which automatically detect and registers student attending a lecture. We have also used Raspberry PI to remotely control the camera and transfer the captured images to the main back-end system. Face detection and Face recognition techniques are briefed in section II and III respectively. Proposed model is discussed in section IV, followed by Results in section V and finally the Conclusion in section VI. FACE DETECTION HAAR CASCADES IN OPENCV Face detection is done using Viola Jones algorithm using Haar cascades in OpenCV, as the classifier. The algorithm needs a lot of positive images and negative images to train the Haar cascades classifier. Positive images are images with clear faces where negative images are those without any faces i.e, the background noise. OpenCV - Open source Computer Vision library is a library of programming functions written in C/C++ or Python aimed mainly at real-time computer vision. The application of OpenCV includes 2D and 3D feature toolkits, facial recognition system, object identification and motion tracking. OpenCV comes with a trainer as well as a detector. OpenCV already contains many pre-trained classifiers for face, eyes, smile etc. FACE RECOGNITION EIGEN FACE APPROACH AND PCA ALGORITHM The eigen face approach involves the following steps. As a first step the system should be initialized with training set of each faces. Next, when a face is detected, the eigenface/eigenvector is calculated for that face. Then, the system compares the eigenvectors of the current face and the stored face image and determines whether the face is identified or not depending on a predefined threshold value. The final step(optional) is that if an unknown face is detected repeatedly the system may learn to recognise it [7]. Principal Component Analysis(PCA) algorithm is used as dimension reducing technique to analyse face recognition issues. It is also known as Eigen Face Projection. By means of data compression, PCA technique reduces the dimension of the images in the training set and shows the most effective low dimensional structure of the facial features. This dimensional reduction accurately disassembles the face structure into orthogonal components known as Eigen faces by eliminating the background noise [1]. Mathematical steps of the PCA algorithm[6,8,9] : Input : original images from the training set in the form N x N data matrix. M such training set images are converted into face vectors( τ i ). Normalize the face vectors by removing the common features of each faces represented by the average face vector( Ψ ). Normalized face vectors, Φ i = τ i - Ψ Calculate the Eigen face vectors( U i ) of dimension N 2 x 1, by computing N 2 x M Covariance matrix, C = AA T where A = { Φ 1, Φ 2,..., Φ M } 507 Kunal Kishore, Ozwin Neel Lobo, Pranay Saraiwala, Prarthana R

3 Dimensionality reduction is done on the Eigen face vectors( U i ) by computing M x M Covariance matrix, C = A T A, because computation of high dimension Eigen vectors is time-consuming. The dimension of the reduced Eigen face vectors( V i ) is M x 1. Select k best Eigen vectors where k < M and can represent the whole training set. Each face from the training set can be represented by the weighted sum of the k Eigen faces/ Eigen vectors and the mean average face vector( ψ ) called the Weight vectors, associated with a numerical value known as the Distance. Output : If the value of distance is greater than a predefined threshold value then the face of the person is recognised. PROPOSED METHODOLOGY The proposed attendance system mainly consists of four phases and the working of the system is depicted as follows [4,10]: Image Accession: The system consists of a camera that captures the images of students sitting in the classroom at a predefined intervals of time and image pre-processing is done. Then the image is dimensionally reduced and enhanced in pixels, and are finally sent for face detection. Face Detection: This process eliminates the rest of the background image i.e., noise to highlight the facial area. Pre-processing: Feature extraction is done for distinguishing faces of different student. In this system, eyes, nose and mouth are extracted. It calculates the overall facial structure, distances between eyes, nose and mouth. Face Recognition: The face image is then compared with the training set of images. If a match is found, then the face is recognized. The system uses MySQL Xampp server to update the database and it will automatically update the student s presence in the class to the student s database. Fig 1 : Image accession Fig 2 : Training set 508 Kunal Kishore, Ozwin Neel Lobo, Pranay Saraiwala, Prarthana R

4 Fig 3 : Face detection and Feature extraction Align and crop Proposed working of the system in a classroom is as follows: A camera is fixed in the classroom. The system works in 3 basic steps- Capture, Detection and Extraction, and Comparison with the existing dataset. First the camera authenticates the lecturer and then it captures the class photo at an interval of time, say, for every 20min. After the face detection and recognition process, for every match in the dataset, the student s data is updated in the database for the specific lecturer. Monthly reports are sent to the lecturers and students regarding the present absent lists of the students collected from the attendance-database. Fig 4 : Activity diagram of the proposed system RESULT The Eigen values calculated from the Eigen Vector covariance matrix are rejected or stored in database depending upon the threshold value, thus creating a face space. Calculating the weights and the Euclidean distance, the match is found through comparison. The resultant face with the lowest score is the best and correct match among the list of recognised faces. 509 Kunal Kishore, Ozwin Neel Lobo, Pranay Saraiwala, Prarthana R

5 Fig 5: Face recognition CONCLUSION In this paper, by embedding Raspberry PI with image processing techniques, the automated attendance system can be proven as an efficient attendance system for a classroom. By using this system the chances of human error, time theft and proxies can be reduced. We can implement a reliable and efficient system for classroom attendance which can work for multiple face recognition at a time[4]. In further work, this system can be extended to mobile based face recogntion application using more effective image processing machine learning algorithms[5]. REFERENCES [1] Rekha E, Department of Electronics Engineering, Amity University, Dr. Ramaprasad P, An Effiecient Automated Attendance Management System based on Eigen Face Recognition, IEEE 2017 [2] Anshun Raghuwanshi, Dr. Preeti D Swami, "An Automated Classroom Attendance System Using Video Based Face Recognition", nd IEEE International Conference On Recent Trends in Electronics Information & Communication Technology (RTEICT), May 19-20, 2017, India [3] Ishita Gupta, Varsha Patil, Chaitali Kadam, Shreya Dumbre, Face Detection and Recognition using Raspberry Pi, 2016 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON -ECE) December 2016, AISSMS, Pune, India [4] Priyanka Wagh, Jagruti Chaudhari, Roshani Thakare, Shweta Patil, Attendance System based on Face Recognition using Eigen face and PCA Algorithms, 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), 2015 [5] D. Nithya, Automated Class Attendance System based onface Recognition using PCA Algorithm, International Journal of Engineering Research & Technology (IJERT), ISSN: , Vol. 4 Issue 12, December-2015 [6] Mathana Gopala Krishnan, Balaji, Shyam Babu, Guided by: Mr.K.Rajesh AP-II CSE & Supported by: Dr.A.Uma makeswari AD, "Implementation of Automated Attendance System using Face Recognition.", International Journal of Scientific & Engineering Research, Volume 6, Issue 3, March Kunal Kishore, Ozwin Neel Lobo, Pranay Saraiwala, Prarthana R

6 [7] Shireesha Chintalapati, M.V. Raghunadh, Automated Attendance Management System Based On Face Recognition Algorithms, 2013 IEEE International Conference on Computational Intelligence and Computing Research [8] Ala Eldin Omer, Adil Khurran, Facial Recognition using Principal Component Analysis based Dimensionality Reduction, International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering, 2015 [9] Shruti Sehgal, Harpreet Singh, Mohit Agarwal, V. Bhasker, Shantanu, Data Analysis Using Principal Component Analysis, 2014 International Conference on Medical Imaging, m-health and Emerging Communication Systems (MedCom) [10] Dr. Nita Thakare, Meghna Shrivastava, Nidhi Kumari, Neha Kumari, Darleen Kaur, Rinku Singh, Face detection and recognition for automatic attendance system, International Journal of Computer Science and Mobile Computing, April Kunal Kishore, Ozwin Neel Lobo, Pranay Saraiwala, Prarthana R