Student Attendance System Based on Face Recognition and
Machine Learning
Praveen K. Sah 1, Mamata Garanayak 2, Sujata Chakravarty 3, Bijay K. Paikaray 4,
Rakesh Sharma 5 and Suneeta Satpathy 6
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Dept. of CSE, Centurion University of Technology and Management, Odisha, India.
4
School of Information & Communication Technology, Medhavi Skills University, Sikkim, India
6
Dept. of Management Studies, Sri Sri University, Odisha, India
Abstract
In colleges and school’s teachers take attendance or students have to sign on to register to mark
their presence. Due to human errors, many times students face problems while getting
attendance. This paper portrays a system that is capable of storing the information of students
by facial recognition of the students present in the class. In this paper various machine learning
and deep learning algorithm has been applied like Support Vector Machine (SVM), Decision
Tree (DT), and Convolutional Neural Network (CNN) along with some pre-trained models like
VGG19 and ResNet50 which is provided by karas application. The model gives a satisfactory
result with an accuracy of 96.82 % when applied on the CNN model and gave 96.97 % accuracy
when applied on the ResNet50 pre-trained Model. After that the best model has been saved for
system implementation based on their performance and applied the detection technique for
detect the face of student and maintain their present in the excel sheet.
Keywords
SVM, CNN, ResNet50, DT, VGG19, Machine Learning, Deep Learning, Attendance System.
1. Introduction
There are diverse biometric frameworks dependent on face recognition, iris, fingerprint, palm-print,
etc. yet in the most of the cases, facial recognition is utilized as a prominent innovation [1]. The
technology which is capable of matching human faces with digital images comes under facial
Recognition. Facial Recognition is used in various fields like Security checking, authentication systems
in offices, creating databases for various identification documents. It is used in mobile phones and
digital cameras for taking better pictures. It is also used in various social networking sites where people
upload pictures and edit them [2]. The system mentioned in this paper analyzes different pictures, store
it in a database which can further be extracted for Attendance purposes in schools and colleges during
live lectures. It will reduce human efforts and human errors. In this system, Attendance is recorded
when a human face is detected in the camera and is matched with the database [3]. Machine Learning
and Deep Learning are a part of Computer Science which creates a system by learning methods from
various sample of data and behaving like a trained Model. The features of the image are the input data.
It represents the behavior of the image [4].
2. Literature Survey
Nirmalya Kar [5] has used two main components for implementation approach computer vision
library (OpenCV) and Fast Light Tool Kit (FLTK). One of Compute vision provides a real-time object
detection Computer vision library. It also used in machine learning for object detection. And another
one FLTK is used for graphical user interface which is developed by Bill Spitz.
ACI’22: Workshop on Advances in Computation Intelligence, its Concepts & Applications at ISIC 2022, May 17-19, Savannah, United States
EMAIL: praveensah15000@gmail.com (A. 1); mamatagaranayak@gmail.com (A. 2); chakravartys69@gmail.com (A. 3);
bijaypaikaray87@gmail.com (A. 4); 190301120079@cutm.ac.in (A. 5); suneeta.s@srisriuniversity.edu.in (A. 6)
ORCID: 0000-0001-5843-0335 (A. 4)
©️ 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
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Sujata G. Bhele [6] In this paper mostly worked on the machine learning and deep learning models
like SVM, ANN, CNN etc. which can help the model to perform the better result, here best model has
chosen which can give true results. This paper explained various features extraction technique or
algorithm like PCA, LDA etc. In this paper some other techniques have used which has normalize the
size of image that is mostly affected the accuracy result.
Riddhi Patel [7] has explained the summary of the face recognition and discusses the techniques
and their working flow. It is also finding the differences between some face recognition models. It
highlights the methods which has given good result as compares to other.
Dwi Sunaryono [8] has given different approaches about the attendance system based on face
recognition. In this paper has explained about the how these types of system can helpful in different
sectors like company employment, schools and colleges to avoid the mistakes. It has mostly focused on
the school or colleges in which most of students has getting their presents by proxy.
Shireesha Chintalapati [9] has proposed a brief summary about face recognition system and
discussed some dimensionality and machine learning algorithm like PCA, LDA for feature extraction
or dimensionality reduction and SVM, Decision tree machine learning algorithms. In this paper also
mentioned some errors which mentioned the student details who has not present in the class which
suggest that add more features to this type of system.
Jomon Joseph [10] has proposed the brief summary of the face recognition and explained the
methods of their working. In this paper the dataset collected by using mobile camera. And used
algorithms like SVM, CNN and some features extraction techniques like PCS. In this paper also given
some information about MATLAB which is the multi-paradigm programming language and numeric
computing environment, which created by MathWorks. MATLAB gives the platform to plotting of
various work like functions, data, implementation of algorithm, creation of user interface etc.
3. Data Description
The dataset used in this paper has been prepared through collection of the images of students and
some images have been collected from the internet. And after that work has move forward to separate
the students’ images in two database folders. Based on the student who is the member of that class and
who is not the member of that class and all the images renamed with their name and registration which
help the system to confirm the student attendance according to their name and registration number. In
dataset the data has different size, shape and colour which can create the difficulties to machine for
resolving these types of problem the data has pre-processed in the upcoming parts of working direction
[11].
3.1 Decision Tree Methodology
Decision tree is a supervised machine learning algorithm or technique which can be used for
solving both types of problem like classification and regression, but it is moreover preferred for
classification. This is one of the easiest and most versatile structures for classification problems. It is
fundamentally a tree of decisions that create nodes where branches are the split of the tree. Each node
along with the sub-nodes is a decision relying on values of defined variables which end with the
classification of every element into any one of the classes. The root is the first variable that divides the
dataset and from here everything starts. Every decision is known as the node and the line which
connects these decisions are called the branch [12].
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Figure-1: Methodology of Decision Tree
3.2 Support Vector Machine Methodology
SVM is a Supervised Machine Learning Algorithms which can be applied in both types of
problem like Classification as well as Regression problems. In majority of cases, it is used for
classification problems under Machine Learning.
SVM’s main motive is to create an appropriate line or which can identify n-dimensional
space into classes which allow us to keep the new data point under appropriate category later.
It handles various linear and non-linear problems and works perfectly on practical problems
[12]. The best limit choose is termed as Hyperplane, which divides the data into classes. Data
is applied on sigmoid kernel of SVM algorithm.
Figure-2: Methodology of Support Vector Machine
3.3 Convolutional Neural Network Methodology
Convolutional neural networks, i.e., ConvNets were first comes to existence in the 1980s.
CNN is composed of multiple layers of artificial neurons [13]. The behavior of each neuron is
decided by its weights. It is a classification structure for classifying images into various labeled
classes [14][15]. The different layers of CNN take out image features and learn to categorize
the images. It is one of the types of feed-forward neural network in DL and AI. CNN has the
capability to extract the each and every portion of input image, which is known as by name
receptive field. It is assigning the weights for each neuron based on the significant role of the
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receptive field. So that it can be discriminate or find the difference between the importance of
neurons from one another [12].
Figure-3: Convolutional Neural Network Methodology
3.4 VGG-19 Methodology
VGG stands for Visual Geometry Group. VGG-19 is a type of convolutional neural network
that has19 layers deep. It is a type of VGG model which contains the 16 convolution layers, 3
fully-connected layer, 5 MaxPooling layers and 1 SoftMax layer. It can be used for the facial
recognition purposes. Its weights are easily available along with other frameworks like karas
[16].
3.5 ResNet-50 Methodology
ResNet50 is the type of Resnet model of karas which contains 48 convolutional layers along
with 1 MaxPooling and 1 Average Pooling layer. It has 3.8 x 10^9 Floating points operations.
This is the most usable Resnet model. It can be also used for computer vision tasks like
classification of images, localization of objects, and detection of objects [13]. This framework
can also be applied to non-computational vision tasks to reduce the computational expenses
and give them the benefit of depth.
4. Proposed Methodology
The methodology proposed here for facial recognition and storing the attendance is entirely
based on machine learning algorithms. States the flow diagram of the working of our system.
Data Preprocessing
In data pre-processing step the dataset has imported to the working directory with the help
of python library i.e., pandas, OS and OpenCV. Then the data has combined or converted to a
list directory in which the both dataset students image data and other members who is the not
the part of that class has concatenated with the help of OpenCV function [17].
i. Face Cropping
The main aim to applying the face cropping is that the images contain the face as well as
the other part. By using this we can crop the face for system use that can give the better
result without interruption any kinds of this type issues. The face cropping has performed
in this paper by using OpenCV python package which have the capacity to handle this
type of problem [13].
ii. Image Reshaping
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In this part of paper, the image has collected in different shape by using different types of
cameras. So, here all the images have converted to equal shape with (224 X 224) resolution
which is also suitable for karas application pre-trained model for better result. And after
reshaping the whole images has converted to 1-Dimensional array which can be easy to
work with the machine learning model [18].
iii. Remove Noise
The proposed on this step to remove the noise from the images that means reduce the noise
like common cause, white noise, remove brightness and colour information in images that
can visualize easily and make smooth to use in machine learning for better performance.
Figure-4: Data Pre-processing
Feature Extraction
The proposed of this process to extract the features from the image which is most useful for
the model implementation the features of any human image like nose, eyes, hear, ear etc. the
new reduced data will be have the capability to summarize the most of the features of that
image after features extraction. In machine learning there are different feature extraction
techniques available like PCA, LDA, ICA etc. [19].
Splitting Data
In the step of splitting the data has slitted or divided for training and testing format. In
training we are train the model by using most of the features that make the system to work with
testing data for good result if our machine learning model learns in better way then it may
perform good that is more generalize and ability to solve the problem statement. Mainly 70 to
80 percent of data has taken for training the machine and 20 to 30 percent of data has taken for
testing the performance and work criteria of the machine [20].
5. Proposed Work
Here in this paper has loaded the model like SVM, Decision Tree, CNN, VGG19 and
ResNet50. And fit all the model one by one using training dataset. Which has taken 75% of
data for train the model and 25% data for testing purpose which can evaluate the work
performance on the basis of test data.
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Result Analysis
In the result analysis the paper has find the confusion matrix of their performance by using
test data set and accordingly find the precision, recall, accuracy and f1-score. And compare the
result that is predicted by different model and find which model has given better result as
compare to another model.
In this paper the support vector machine (SVM) has given 56.70% of accuracy result,
Decision tree has given 71.89% of accuracy result, convolutional neural network has given
96.82% of accuracy result, VGG19 which is a pre-trained model has given 96.60% of accuracy
result and ResNet50 is also a pre-trained model has given 93.97% of accuracy result [14].
Figure-5: Experimental Result
As the graph shows CNN gives the best F1 score and accuracy with96.82% and 89.00%
respectively followed by VGG19 with 96.60% and 78.43% respectively as compared to other
models. SVM gives the least result with 56.70% accuracy and 48.89% F1 score.
After comparing the result, the model has been saved for next level implementation for
check the working of the project. Here the paper has work with video processing by using
OpenCV which will open the virtual camera and detect the face of students [19]. If there the
student is the member of that class according to that the system will display the name and
registration number of student and puts their attendance present in the .csv file [21].
Table 1
Result Analysis
SL No. Model Accuracy F1-Score
1 Support Vector Machine 56.70% 48.89%
2 Decision Tree 71.89% 64.20%
3 CNN 96.82% 89.00%
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4 VGG19 96.60% 78.43%
5 ResNet50 93.97% 75.78%
Model Accuracy and F1-Score
150.00%
100.00%
50.00%
0.00%
SVM Decision Tree CNN VGG19 ResNet50
Accuracy F1-Score
Figure-6: Accuracy Result
6. Conclusion
The system proposed in this paper uses OpenCV Computer Vision along with various
machine learning and deep learning technique. Facial detection carried out in this paper is done
by using OpenCV. It will reduce human errors and will help in reducing human efforts. The
collected data is of students which were applied for training and testing purposes. When tested
on different models like SVM the accuracy that we got was 56.70% and the F1 score for this
machine learning model was 48.89%. When the data was applied on Decision Tree classifier
the accuracy was 71.89% with the F1 Score of 64.20%. When the data was applied on CNN it
gave a satisfactory result of 96.82% with a very good F1 score of 89.00 %. When the same data
was applied on some pre-trained models like Vgg19 and ResNet50, we got the accuracy of
96.60% and 93.97% respectively along with F1 score of 78.43% and 75.78% respectively. In
this paper after the implementation in practical use the attendance data can be extracted in the
form of .csv file that helps in managing attendance record of the students as per their presence
in the class on a daily basis. So, through this result it can be stated that the paper can be used
for attendance purpose in various Schools and colleges. Further the paper can be developed for
use in employment sector for creating records.
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