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Volume 12, Issue 04 (April 2023)

Facial recognition attendance system using machine learning and deep learning.

face recognition attendance system thesis

  • Article Download / Views: 679
  • Authors : Shashank Joshi , Sandeep Shinde , Prerna Shinde , Neha Sagar, Sairam Rathod
  • Paper ID : IJERTV12IS040073
  • Volume & Issue : Volume 12, Issue 04 (April 2023)
  • Published (First Online): 24-04-2023
  • ISSN (Online) : 2278-0181
  • Publisher Name : IJERT

Creative Commons License

Shashank Joshi

Computer Science Vishwakarma Institute of Technology,

Pune, India

Prof. Sandeep Shinde

Prerna Shinde

Sairam Rathod

Abstract – The old method of marking attendance involves the lecturer providing an attendance sheet to the students for their signature or the teacher calling out students name individually to mark them present. This old manual method is pretty hectic for teachers and students too. Since, after taking the signed attendance sheet from students, teachers have to manually keep track of every student in the logbook which turned out to a lot of time wastage, missing out students presenteeism or students giving proxies for the absentees due to which logbooks can be easily manipulated or prone to errors also, wastage of pen and paper. To avoid this problem, we have developed a system which will monitor the attendance of students by identifying their faces via their facial features. While developing this system we have used a web Camera to capture multiple live images of students for face recognition, Viola-Jones Algorithm to achieve face detection which uses Haar Cascade classifier, Pre- processing which converts the image in greyscale, LBPH algorithm and deep learning algorithms like CNN (Convolutional neural networks) for feature extraction and last but not the least the input faces are then matched with the trained images in the database and once they match, the student will be marked as present and the ones who didnt match weremarked as absent in the class. Accuracy of 85% and 95% was obtained by testing the model with ten different faces with different facial expressions, angle and lighting conditions for LBPH algorithm and CNN (Convolutional neural networks) respectively.

Keywords Face recognition, Face detection, feature extraction, LBPH algorithm, Viola-Jones algorithm, Haar Cascade classifier, CNN (Convolutional neural networks), Deep Learning.

INTRODUCTION

Traditionally marking attendance involves students sitting in a classroom and the teacher calling out the names of the students individually to mark their attendance. The attendance is usually marked with a pen and then stored in a logbook. The traditional attendance system has a lot of

disadvantages it is hard to keep records, it is time- consuming, error prone, wastage of resources.

Another system that is around is a biometrics system. A biometric system has three phases registration phase, storage, and recognition phase. The registration phase involves capturing specific triats. The triat is saved in the form of a graph or code. The recognition phase extracts biometric triat and compares it with the database to see if matches or not.

Face recognition has numerous advantages over biometrics as they require less action from users and multiple attendances can be marked at a time.

The main moto of the paper is to implement an attendance system using facial recognition. Face recognition involves image capture through a web camera, Face detection using viola jones algorithm which uses haar cascade classifier, pre-processing, storing the image in the database, feature extraction through LBPH algorithm and CNN (Convolutional neural networks) then comparing it with the input given by the user and if it matches attendance will be marked if it doesnt attendance wouldnt be marked.

Hence, our proposed system aims to mark attendance automatically by means of face recognition. The teacher can monitor attendance easily.

LITERATURE SURVEY

Poornima, Sripriya, Vijayalakshmi, Vishnupriya proposed a work of monitoring attendance using Facial Recognition in which Audio was generated when the student was absent, and also gender classification was implemented. For audio output, they used speech API and used facial distance measure as a progenitor to achieve the gender

classification. They have used the PCA algorithm for it [1]. Kennedy Okokpujie, Etinosa Noma- Osaghae, Samuel John, Kalu-Anyah Grace, Imhade Okokpujie proposed a system that marks the attendance of students and sends notifications on cell phone using facial recognition technique and GSM. Software used by

them were PostgreSQL, SDK, and fisher face. [2]. Jeevan. M, Yashwanth Gowda. S, Sindhu. J developed a system that recognizes the faces of people moving in a cluster. For face detection, they have used viola- jones and for feature extraction, theyhave used PCA [3]. Shivam Singh, Prof. S. Graceline Jasmine proposed a system that recognizes the faces of people in motion in this paper they have used algorithms like KLT, Viola-Jones for face detection [4]. A Madhu Sudhan, Sudhir Kumar. Sharma developed a system of marking students attendance automatically by using face recognition algorithms like LBP, SVM [5]. Mrs. Madhuram.M, B. Prithvi Kumar, Lakshman

Sridhar, Nishanth Prem, Venkatesh Prasad designed a system that recognizes a persons face by means of an IP camera and image set algorithm using open CV [6]. SudhaNarang, Kriti Jain, MeghaSaxena, AashnaArora proposed a model for monitoring students attendance using OpenCV also, they have compared different face recognition algorithms in this paper [7].

PROPOSED MODEL

Fig.1:Proposed Model

This face recognition system consists of mainly two parts. The first section is while enrolling a student into a course his/her face will be captured and stored in the database. In the second section, while a student is entering the class his/her face will be captured through a camera in the classroom and then recognized, and accordingly, the student will be marked absent or present.

Image Capture- Multiple live images

will be captured through the webcam.

We have included/captured several images of each student with variations like students wearing spectacles or not, students with beard/moustache/clean shaved, etc.

Face Detection-Viola-Jones Algorithm is used for face detection. Below is an explanation of how the algorithm exactly works.

Viola-Jones Algorithm- Given a picture, the algorithmic rule looks into several smaller regions and tries to search a face by finding specific features in each region. This algorithmic rule works on a grayscale image, it must check many different positions and scales because an image can contain many faces of various sizes. This algorithm uses Haar-like features to detect faces.

It has four main steps, that we shall discuss within the sections to follow:

Selecting Haar-like features-Haar-like features are used in recognizing an object. Human faces have some properties like the eye region is dark as compared to its neighbour pixels, and the nose region is brighter than the eye region. In the dark region, the total sum of pixels will be smaller than the total sum of pixels in the light region.

There are three types of Haar-like features that are used in this algorithm.

Edge features

Line-features

Four-sided features

Fig.2:Haar-like features

Creating an integral image-To calculate a value for each feature, we need to perform computations on all the pixels within that specific feaure. Actually, these calculations can be very large, so the integral image allows us to perform the calculations easily. An integral image, the value of each square is the total sum of all pixels to above of it plus, the sum of all to the left side of it plus, the pixel at the square itself.

Running AdaBoost training-The range offeatures present in the detector is approximately 160,000, but only a some of those features play a vital role in detecting a face. So, we use this algorithm to identify the best of all of the available features.

So, when were training the AdaBoost to identify important features, the algorithmic rule sets a minimum boundary to inform whether some feature can be classified as useful or not when we have trained the algorithm according to the input.

Creating classifier cascades-The cascade classifier eliminates all the non-faces instead of wasting time in computations. In this way, speed can be increased. At every stage, the classifier eliminates the non-face.

Fig.3:Face Detection

Pre-Processing- In this, the image is converted into greyscale. Then these detected images are stored in the data base.

Fig.4:Image after Pre-processing

The images undergo feature extraction using the LBPH algorithm and Convolutional neural networks (CNN). Students are given unique roll no. Based on the facial features and unique roll no the model is trained.

Fig.5:Training Database

Recognition

Image Capture The real-time images of the students are taken as input through a web camera.

Face Detection After the image is pre- processed it is then, treated with the Voila-Jones algorithm for the process of face detection.

Pre-processing This process mainly involves converting the coloured image into grayscale. This is achieved by passing it through the Gaussian Filter and Median Filter. In image processing, Gaussian Filter is known as linear filter wherein, the Median filter uses the non-linear filter technique. The two of them can be used for unsharp masking. Where Gaussian filter blurs edges of the image and also reduces the contrast wherein, Median filter reduces the noise present in an image by keeping edges relatively sharp.

Feature Extraction The images undergo feature extraction using the LBPH algorithm as it least affected by light variation. The idea with LBPH is not to look at the image as a whole, but instead, try to find its local structure by comparing each pixel to the neighbouring pixels.

In LBPH we tend to compare each picture element(pixel) of the image with its neighbouring pixel. Center pixel is thresholding picture element(pixel) and its value may be an edge value. The neighbouring pixel values are compared with the edge value. If the value is more than edge value then it is set to one (1) and if it's less than the edge value then it is set to zero (0). This value is regenerated to binary by concatenating all the neighbour pixels. The pixels will concate while moving in a clockwise direction or anticlockwise direction.In this algorithm we tend to concate pixels in clockwise direction and convert it to binary.

From obtaining value in binary, we tend to convert it into decimal. Then each picture pixel of image assigns a brand- new value. From the new value of the image, we make a histogram. All the local histogram is concatenating to get a feature vector. This is how Local Binary Pattern Histograms (LBPH) works.

Fig.6:LBPH Architecture

Feature extraction can also be done by deep learning .In deep learning Convolutional neural networks(CNN) is used.

Convolutional neural consists of convolutional layers,

pooling layers, fully connected layers .The model consists of 5 convolutional layers with max pooling and 3 fully connected layers. Max pooling is done to reduce dimensionality.This architecture is also known as Alexnet. It reduces the number of parameters and computations.

Relu activation function is used in each layer except the output one. Using non linerarity of Relu CNN can be trained six times faster than functions like tanh or Sigmoid. To prevent overfitting data augmentation is done. Crops sized 227×227 from inside the

256×256 image boundary are used for data augmentation. Dropout is done to prevent overfitting. A neuron is dropped with a probabitlity of 0.5. Doing this a more robust model is created. Alexnet accepts image of size [227x227x3] as input. For face recognition 2000 images per person are taken for higher accuracy . All the images are associated with the roll no of the student. The roll and name are in the hashmap form in the code. So the model can print the roll no as well as name in the excel sheet when the input is given and. Accuracy of 95% was achieved by using alexnet.

Table.1:CNN Architecture

Matching and Attendance The inputface images are compared with all face images stored in the training dataset, one by one. The images of the face of students were matched with the images available in the training dataset, once they match the student will be marked as present and the ones who didnt match were markedas absent in the class.

Fig.7:Face Recognition

Fig.8:Face Detection

Fig.9:Image After Pre- processing

Fig.10:Training Database

Fig.11:Face Recognition

FUTURE SCOPE

Frauds in ATM can be prevented. The facial recognition system will become more robust in the future as continuous improvement is being made in it. It will only authorize customers the entry.

It can likewise be utilized during exams, for example, Civil Services Exam, SSC, IIT, MBBS, and others to distinguish the competitors.

Duplicate electors are being reported all over the world. To stop this, it will only allow authorized voters with id to vote.

This framework will widely be used in government and corporate offices all over the world.

First step of Face recognition model involves image capture through a web camera. Second step involves Face detection using viola jones algorithm which uses haar cascade classifier. Third step involves pre- processing, this process mainly involves converting the coloured image into grayscale. This is achieved by passing image through the Gaussian Filter and Median Filter. Fourth step involves storing the image in the database. Then feature extraction through LBPH Convolutional neural networks (CNN). Then comparing it with the input givenby the user and if it matches attendance will be marked in the csv file, if it doesnt attendance will not be marked. GUI is also developed using tkinter in python.

Table.2: Results obtained by different facial expressions, angle and lighting conditions by LBPH.

Table.3: Results obtained by differentfacial expressions, angle and lighting conditions by Convolutional Neural Network(CNN).

Accuracy=average of accuracy obtainedby testing with ten faces. For LBPH=850/10=85% For CNN= 950/10=95%

Table.4:CSV file of attendance

Fig.12: GUI for face recognition VI.CONCLUSION

This system works very well as compared to

other systems which uses eigen faces, fisher faces, as we have used the most efficient algorithms here, that is Viola- Jones Algorithm used for face detection and the LBPH algorithm and Convolutional Neural Network (CNN) for feature extraction.

Accuracy of 85% is obtained by using LBPH algorithm. Accuracy of 95% is obtained by using CNN. This system also has confidence added to it which when only above 70% marks the attendance otherwise it doesnt mark the attendance Also, this system can recognize multiple faces, so that even the whole class can give attendance at thesame time. An image on a cell phone or a photocopy wont work while marking attendance, that is a student must physically be present for his/her attendance to be marked. In this way, proxy attendance using pictures on mobile phones can be avoided.

So, every institute or workplace must implement a facial recognition attendance system as the fingerprint system cant mark multiple attendances at a time and people may end up waiting in queues

VII. REFERENCES

ISSN: 2278-0181 Vol. 8 Issue 05, May-2019

3,February 2020

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Face recognition Attendance System Using Face Recognition Technique

Authors subhankar pal indranath sarkar sourav mondal sayantan mitra soumyajit khan pritam chakraborty kousik maity.

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The main purpose of this project is to build a human face recognition for an institute or organization to mark the attendance of their students or employees. It is a subdomain of Object Detection, where we try to observe the instance of semantic objects. This system is fully automated and easily deployable.

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Automatic Attendance Management System Using Face Recognition

Profile image of hazem ahmad

Being one of the most successful applications of the image processing, face recognition has a vital role in technical field especially in the field of security purpose. Human face recognition is an important field for verification purpose especially in the case of student's attendance. This paper is aimed at implementing a digitized system for attendance recording. Current attendance marking methods are monotonous & time consuming. Manually recorded attendance can be easily manipulated. Hence the paper is proposed to tackle all these issues.

Related Papers

Navesh Sallawar

Students attendance in the classroom is very important task and if taken manually wastes a lot of time. There are many automatic methods available for this purpose i.e. biometric attendance. All these methods also waste time because students have to make a queue to touch their thumb on the scanning device. This work describes the efficient algorithm that automatically marks the attendance without human intervention. This attendance is recorded by using a camera attached in front of classroom that is continuously capturing images of students, detect the faces in images and compare the detected faces with the database and mark the attendance. The paper review the related work in the field of attendance system then describes the system architecture, software algorithm and results.

face recognition attendance system thesis

International Journal for Research in Applied Science & Engineering Technology (IJRASET)

IJRASET Publication

In the recent time automated face recognition has become a trend and has been developed very much , this is mainly due to two reasons; first it is due to availability of modern technologies and second is due to the ability to save time using face recognition in the process of taking attendance of students. Its usage will grow vast in the future as it saves a lot of time. It consumes a lot of time to take attendance manually and few might also fake the attendance, in order to prevent time consumption and avoid faking the attendance face recognition is used to identify the person present in the class and mark his attendance , this is done with the help of image or video frame. We proposed an automatic attendance management system using machine learning techniques such as CNN algorithm. The face detection and recognition will automatically detect the students in the classroom and mark the attendance by recognizing the person.. The faculty has access to add the student details such as name, USN, phone number, email-id. Then the image is captured through a high definition camera during the class hours. When the lecturing is going on faces of students are detected, segmented and stored for verification with database using the Convolutional Neural Networks (CNN) algorithm of machine learning technique

naveed baloch

The Oxford Dictionary defines a face because the a part of a man or woman's head from the brow to the chin. In human interactions, the face is the maximum giant issue as it incorporates vital facts approximately an person. All people will well known human beings from their faces. The proposed answer is to broaden an running prototype of a gadget that could facilitate elegance attendance control for the academics withinside the school rooms through detecting the faces of students from an photograph taken in a school room. The database can shop the faces of students, as soon as the face of the person fits with one in all of the faces held withinside the database then the attendance is recorded. In latest years, evaluation has been allotted and face reputation and detection structures are advanced. A wide variety of those are used on social media structures like Facebook, Banking apps, authorities offices, etc. In today's aggressive world, with very much less school room time and growing operating hours, teachers can also additionally want equipment that may assist them to manipulate treasured elegance hours efficiently. Instead of that specialize in teaching, teachers are caught with finishing a few formal duties, like taking attendance, keeping the attendance document of every scholar, etc. Manual attendance marking unnecessarily consumes school room time, while clever attendance via face reputation strategies enables in saving the school room time of the lecturer. Attendance marking via face reputation may be implied with inside the school room through shooting the photograph of the scholars with inside the school room thru the digital digicam installed.

International Journal of Scientific Research in Science and Technology

International Journal of Scientific Research in Science and Technology IJSRST

At the beginning and end of each session, attendance is an important aspect of the daily classroom evaluation. When using traditional methods such as calling out roll calls or taking a student's signature, managing attendance can be a time-consuming task. The teacher normally checks it, although it's possible that a teacher will miss someone or some students' answers many times. Face recognition-based attendance system is a solution to the problem of recognizing faces for the purpose of collecting attendance by utilizing face recognition technology based on high-definition monitor video and other information technology. Instead of depending on time-consuming approaches, we present a real-time Face Recognition System for tracking student attendance in class in this work. The suggested method included identifying human faces from a webcam using the Viola-Jones technique, resizing the identified face to the desired size, and then processing the resized face using a basic Local Binary Patterns Histogram algorithm. After the recognition is completed, the attendance will be immediately updated in a SQLite database with the relevant information. Many institutions will profit greatly from this endeavor. As a result, the amount of time it takes and the number of human errors it makes are minimized, making it more efficient.

International Journal of Computer and Communication Engineering

Dr. Ashim Saha

In this era of digitalization, everything is interlinked and are online. Maximum of things are using ML (Machine Learning), AI (Artificial Intelligent), IoT, Data Science etc. Making use of this, an automated attendance system can be built. So, this project is proposing "Digital Attendance System" using "Face Recognition Technique". Entering and keeping information in database and using algorithm to extract the face features, this way face recognition technique is achieved. And this technique is used to compare the captured image of source with that of database, resulting in Digital Attendance System which can be used to mark the attendance and so the motive is achieved.

IJIRST - International Journal for Innovative Research in Science and Technology

Face recognition is the identification of humans by the unique characteristics of their Faces. Face recognition technology is the least intrusive and fastest bio-metric technology. It works with the most obvious individual identifier the human face.This research aims at providing a system to automatically record the students’ attendance during lecture hours in a hall or room using facial recognition technology instead of the traditional manual methods. The objective behind this research is to thoroughly study the field if pattern recognition (facial recognition) which is very important and is used in various applications like identification and detection.

Ise A Orobor , Ofualagba Godswill

Attendance management system is a necessary tool for taking attendance in any environment where attendance is critical. However, most of the existing approach are time consuming, intrusive and it require manual work from the users. This research is aimed at developing a less intrusive, cost effective and more efficient automated student attendance management system using face recognition that leverages on cloud computing (CC) infrastructure called FACECUBE. FACECUBE takes attendance by using IP camera mounted in front of a classroom, to acquire images of the entire class. It detect the faces in the image and compares it with the enrolled faces in the database. On identification of a registered face on the acquired image collections, the attendance register is marked as present otherwise absent. The system is developed on Open Source image processing library hence, it is not vendor hardware nor software dependent.

International Journal of Engineering and Advanced Technology

Shahriar Anik

The most common difficulty that every teacher faces in class room is to take the attendance of the students one by one in each and every class. For the time being many automated systems has been proposed for taking student attendance. In this paper, I introduced an automated student attendance system based on the use of unique techniques for face detection and recognition. This system automatically detects the student when he or she enters the classroom and recognizes that specific student and marks the student's attendance. This method also focuses on the specific features of different attributes such as the face, eye and nose of humans. In order to evaluate the performance of different face recognition system, different real-time situations are considered. This paper also suggests the technique for handling the technique such as spoofing and avoiding student proxy. This system helps track students compared to traditional or current systems and thereby saves time.

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