Copyright © 2021, Nurul Akbar Tanjung. This is an open access article distributed under the Creative Commons Attribution License, which permits
Face Recognition using Webcam with K Nearest Neighbors Algorithm for Employee Presence
Nurul Akbar Tanjung1,*, Sanwani2
1 Perbanas Institute, Jakarta, Indonesia
2 Nusa Mandiri University, Jakarta, Indonesia
Email: 1,*[email protected], 2[email protected] Coressponding Author: [email protected]
Submitted: 13/11/2021; Accepted: 30/11/2021; Published: 30/11/2021
Abstract−Attendance is an activity to store data related to employee attendance. Therefore, it is necessary to have a presence with a biometric identification system such as facial identification so that the presence can run quickly and at a low cost. Attendance system helps employees and companies to run attendance faster and cheaper. The K Nearest Neighbor algorithm has a function as a classification algorithm in machine learning. The K value which is the highest accuracy by reaching 100% is 4 people with the determination of K equal to 3.
Keywords: Presence System; Face Identification; Classification; KNN; Accuracy
1. INTRODUCTION
The presence of employees in participating in work activities in an organization is something that must be managed properly. Every employee who attends generally requires concrete evidence when working, one of which is the presence of a list. In general, organizations apply batch processing to obtain a recapitulation of attendance from each employee. Attendance recorded every day will be collected and then included in the process of calculating employee salaries which are generally carried out regularly every month.
Attendance is divided into two types, namely manual and automatic attendance. Manual attendance has the disadvantage that it can be falsified because it is only limited to initial signatures, while automatic attendance has many advantages such as not easy to forge, well recorded, and can provide efficiency to related organizations. Automatic attendance generally applies biometric technology that utilizes biological aspects, especially the unique characteristics possessed by humans. Presence is one form of approach to authentication of employee data. Authentication is an approach to prove something authentically. There are several types of biometric characteristics that can be used for automatic attendance identification, including fingerprints, irises, faces, voices, hand geometry, and signatures.
(signature). The biometric characteristic used in general is fingerprint identification[1]. There are several variations of the process carried out in identifying fingerprints, for example identification with only one finger, identification using five fingers, and further processing of two-dimensional or three-dimensional fingerprints. Fingerprint identification has a weakness because it requires a high degree of correlation with the system. Examples of problems that can arise include if a person's fingerprint has been lost or changed, the employee attendance process cannot be carried out.
Installation costs of fingerprint identification systems are generally expensive. One of the technology trends that has become an alternative solution for these biometric characteristics is face identification. Facial identification has several advantages such as ease of use and requires low maintenance and installation costs[2]. The development of machine learning technology plays a big role in terms of employee attendance input, one of which is by identifying facial similarities by applying related algorithms. Like previous research regarding face identification for student attendance in class using several algorithms, such as k-nearst neighbors, linear discriminant analysis, principal component analysis[3]. In this study, k-nearst neighbors had the highest accuracy of 90.35%. Therefore, k-nearst neighbor is one of the classification algorithms in machine learning for computer vision that can be applied so that employee attendance at PT Adroady can be properly recorded with high accuracy.
PT Adroady is a company engaged in advertising using information technology. Until now, PT Adroady does not yet have a well-automated attendance system. Some of the problems that arise as a result of this include the presence of not being managed properly so that it has an impact on the sustainability of the organization. Therefore, this study aims to provide a face-based employee attendance system based on a website. Based on the problems that have been described, it is necessary to have an employee attendance system that utilizes biometric technology automatically so that every employee present is identified properly to facilitate PT Adroady in collecting attendance data for each employee.
2. RESEARCH METHODOLOGY
2.1 Presence
Attendance is an important criterion that can be used for various purposes. In various sectors, attendance is closely related to the discipline and performance of human resources and reflects one's commitment to the organization.
employee performance is the final result of the assessment or work achieved from an implementation of duties and responsibilities that describes how well employees in a company fulfill their work[4].
The results of the attendance data report indirectly can also provide the ability for management to plan optimal financing needs and determine best practices in optimizing their workforce. In the field of education, minimum attendance calculations are also required in the education regulations and assessment components. Attendance data retrieval can be done manually, by using sheets of paper and collecting signatures or providing an attendance checklist.
However, this manual method has many weaknesses, including being very easy to fake and also very time consuming.
In relation to the world of work, the presence of an employee shows his commitment to the work and the company. describes several factors that can affect employee attendance in a model in Figure 1.
Figure 1. Employee Attendance Factor
Employee attendance system is an activity or routine carried out by workers or employees to prove that they have been present or have come to carry out their responsibilities in working in a company. Recording employee attendance is one of the instruments that is an important factor in human resource management (HR). A few percent of all companies still use traditional or manual attendance systems such as signatures or timesheets. The process of filling out the attendance list manually or traditionally has many shortcomings in accuracy which can be an inhibiting factor in the process of monitoring the level of employee discipline in terms of recording the punctuality of coming and going home. That employee attendance is one of the five elements of human resource performance achievement that shows the quality and quantity of human resources for a period of time in carrying out their work assignments[5]. So that the employee attendance system is a system that records and processes employee attendance data consisting of employee data, entry and exit hours and attendance date data.
At PT. Adroady with 60 employees located in Jakarta which is the capital of the Republic of Indonesia, it cannot be denied that road congestion and congestion are one of the main problems for workers. Time constraints, road congestion, and the number of vehicles are one of the main factors that affect the level of punctuality of attendance.
This requires a supportive commitment from all human resources, both from the company and employees. Commitment to improving human resources has also become a research topic that has been widely considered for research in recent time periods.
2.2 Computer Vision
Computer vision is a combination of a number of automated processes with the intent to produce visual perception, such as image processing, recognition and decision making. The approach in computer vision seeks to adopt how the human visual system (human vision) works, how the human sense of sight (eyes) works when looking at objects, then forwards the image of the object to the brain for the interpretation process so that what object appears in human eyes can understand[6]. The results of this interpretation process can then be used in decision making. The human vision system has an extraordinary ability to perform segmentation to distinguish between an object and its surroundings, this is in accordance[7]. Techniques in computer vision can be used to analyze various types of data, not only limited to images from cameras. In the medical field, MRI (Magnetic Resonance Imaging) images are known to represent the body's cell tissues and identify diseases such as tumors or cancer. Various spatial data can also be obtained from sensors to produce image representations that can be processed. Various types of images that can be processed using computer vision techniques are shown in Figure 2.
Figure 2. A number of examples of real representations of images obtained from various sources.
Various image processing and computer vision techniques are implemented using special software and coding techniques which are expected to maximize the image transmission process. This system allows processing in Joint
Photographic Expert Group (JPEG) to Moving Picture Expert Group (MPEG) formats with several widely used programming languages, namely Python, C, C++ and Java. Table 1 shows various software packages that can be used in the application of computer vision for interface creation, implementation in the fields of robotics, biometrics and system security requirements. So far, OpenCV is one of the most popular among others.
Table 1. Various software packages that support the field of computer vision
Software Package Source Link
OpenCV Intel opencv.org/
VXL Various international contributors vxl.github.io/
Cimg Various international contributors sourceforge.net/projects/cimg/
VLFeat Oxford dan UCLA vlfeat.org/
OpenIMAJ Southampton openimaj.org/
2.3 Image
Image is a representation, resemblance, or imitation of an object. The image as the output of a data recording system can be optical in the form of photos, analog in the form of video signals such as images on a television monitor, or digital which can be directly stored on a storage medium[8]. Meanwhile, an image is an image on a two-dimensional plane that is produced from a two-dimensional analog image and is continuous into a discrete image, through a sampling process the analog image is divided into M rows and N columns so that it becomes a discrete image.
Image is a picture or resemblance to an object[9]. Image as a function of two variables f(x,y) and f as the amplitude (eg brightness) of the image at coordinates (x, y). Based on the following description, it can be concluded that the image is a picture of a real object in which there is a lot of information from the object. Digital image is the result of capturing a physical object using digital imaging equipment, where each part of the image is represented in the form of pixels (picture elements). A digital image can be edited, manipulated, sent, deleted, copied or inserted into other computer files or to web pages. The image as the output of a data recording system can be optical in the form of photos, analog in the form of video signals such as images on a television monitor, or digital which can be directly stored on magnetic tape. Computers can only work with finite numeric numbers, so the image must be converted into the form of a finite numeric number (digital image) before being processed in a computer. To convert a continuous image into a digital image, a horizontal and vertical grating process is required, so that an image is obtained in the form of a two- dimensional array. This process is known as the digitization or sampling[10].
2.4 Biometric
Biometrics comes from the Greek, namely bios which means life and metron which means size[11], Biometrics is a method to identify humans based on one or more unique physical or behavioral characteristics. The development of biometric technology is based on the fact that basically every human being has something unique/distinctive. The uniqueness is of course only owned by himself. Biometric technology was developed because it can fulfill two functions, namely identification and verification, besides that biometrics has characteristics such as not easy to lose, unable to forget, and not easy to be faked because its existence is inherent in humans. Where one human being with another will not have the same characteristics that are 100% the same, then its uniqueness will be more guaranteed. The parts of the human body that are unique/specific include fingerprints, the retina of the eye, and the structure of the face.
Biometric technology is individual recognition technology based on specific physical characteristics such as fingerprints, hand geometry, iris, and face and individual behavior such as voice, hand signature and handwriting.
Biometrics is an automated method of recognizing or verifying a person's identity based on a physical characteristic or behavior. Currently there are 7 main areas included in biometric technology, namely: Fingerprint Recognition, Hand Geometry Recognition, Facial Recognition, Iris and Retina Recognition, Voice Recognition, Keystroke Recognition and Signature Recognition. Biometric technology is based on specific physical characteristics and individual behavioral characteristics, including fingerprints, hand geometry, iris, retina, face, voice, signature and handwriting. Biometric identification using faces in the attendance system by implementing the haar cascade method and the PCA eigenfaces algorithm. In this study, it was successful to integrate face detection and recognition into a computerized presence system. In the condition of the face angle orientation to the camera of 00 can produce a recognition rate of 95%. Kar's research is based on an automatic and real-time face detection and recognition process.
Figure 3.Forms of biometric identification
The reason for using biometrics is because of human limitations in verifying based on objects, for example all required data is on an object (such as documents or credit cards). If it is lost then other people can fake it or misuse it. In addition, verification based on knowledge, for example using a password, even if using the best encryption algorithm, there is still a key that can open it. Meanwhile, when compared to using biometrics, it has several advantages such as cannot be lost or forgotten, difficult to duplicate, share or transfer, authenticity is more guaranteed because it must present a person as a validation tool. Some of the things that encourage the use of biometric identification is that biometrics is universal. (found in everyone), unique (everyone has their own characteristics), and not easily faked. With biometric techniques, one does not have to carry an identification device as in conventional techniques. A biometric recognition system, or often called a biometric system, is an authentication system using biometrics. The biometric system will automatically recognize a person's identity based on a biometric feature by matching these features with the biometric characteristics that have been stored in the database. The characteristics of biometrics are first Phsycological, associated with body shape / body. For example fingerprint, face recognition, hand geometry, and iris recognition.
second Behavioral is associated with a person's behavior. For example keystroke, signature, voice.
2.5 Face Recognition
In biometric identification, the face is the most important part of the human body which is the focus of attention in social interactions in all walks of life, the face is a vital role by showing the identity and emotions of each individual.
Face is one of the easiest physiological measures and is often used to distinguish individual identities from one another.
Humans can distinguish faces from one person to another and remember someone's face quickly and easily. The human ability to judge and know a person from his face is extraordinary. This can be an indicator to recognize thousands of faces because the interaction frequency is very frequent or only at a glance even in a very long time span.
Even in some cases a person is able to recognize someone else even though there is a change in that person due to increasing age or wearing glasses or changing hairstyles and so on. Therefore, the face is used as an organ of the human body which is used as an indication of the introduction of a person or face recognition.
Face detection can also be viewed as a pattern classification problem where the input is an image and the output is the class label of the image. In this case there are two class labels, namely face and non-face. Understanding Image in general can be defined as a function of the light intensity of an object in two dimensions. Many face recognition (identification) techniques have used the assumption that the available facial data have the same size and uniform background. In the real world, this assumption does not always apply because faces can appear in images of various sizes, positions, and backgrounds[12]. To read facial characteristics, a reader is needed, a database capable of storing facial pattern data and of course software that can analyze the data. If someone tries to access an area, the system will compare the stored facial pattern with the face pattern that will enter the area. A system that uses a good face recognition algorithm will be able to determine whether a user who is trying to access an area is allowed or not to gain access to that area. The role of information technology is now so fast. Information technology as one of the tools has been widely used to help smooth activities in all fields of work and individual activities. Efficiency and effectiveness is one of the things that causes information technology to be used. In general, an image recognition system does not use bitmap pixels directly, but the system works on the feature domain. Image is represented in the form of a more compact feature which is then used for recognition, thereby saving computations.
2.6 Algoritma K-Nearest Neighbour
One of the classification methods that is often used is the knee best neighbor method. The use of k-nearest neighbor which aims to classify new objects based on attributes and training samples. The k-nearest neighbor algorithm is a method for classifying objects based on the learning data that is closest to the object. The K-Nearest Neighbor algorithm is a supervised learning algorithm, which is a guided learning process where the prediction results of the new instance value are done by classifying new objects based on the attributes and attribute values of the training data by finding the closest distance between the data to be evaluated, namely between new data and data. duration (training data) based on the majority of the category k closest neighbors in training. The best value of k for this algorithm depends on the data.
In general, a high value of k will reduce the effect of noise on the classification, but make the boundaries between each classification increasingly blurred. Definition of the K Nearest Neighbor algorithm is an approach to finding cases by calculating the proximity between new cases and old cases based on matching the weights of a number of existing features and having similarity (similarity) to classify new objects based on attributes and characteristics[13]. training samples.
The K-Nearest Neighbor algorithm is a classification method carried out from learning data that has the closest distance to the object[14]. K Nearest Neighbor algorithm is a classification method that determines categories based on the majority of categories in K-Nearest Neighbor. The K-Nearest Neighbor algorithm has an important component as one of the parameters of this algorithm, namely the K value. The selection of the k value is important because it will affect the performance of the K-Nearest Neighbor algorithm[15]. Based on the description above, the working principle of the K-Nearest Neighbor algorithm is near or far neighbors which are usually calculated using the Euclidean distance with the following formula:
𝐷(𝑎, 𝑏) = ∑ (𝑎𝑘 − 𝑏𝑘𝑑𝑘 )2 (1) Description:
D(a,b) = Distance 𝑎𝑘 = Data testing d = Dimensi data 𝑏𝑘 = Data sample k = Variable data
KNN (K-Nearest Neighbor) is one of the methods for classifying objects based on the learning data that is the closest distance or has the most characteristics in common with the object. Near or far neighbors are usually calculated by the Euclidean distance. This technique is simple and can provide good accuracy of qualifying results.
𝑑(𝑖, 𝑗) = √𝑥𝑖 − 𝑥2 (2)
Description:
d (i,j) = distance value xi = value – value fitur 1 xj = value – value pada fitur 2 2.7 Python
Python is an object-oriented high-level interpreter programming language created by Guido van Rossum. Python has a simple and straightforward syntax, which makes it a very suitable language for beginners who want to learn computer programming. Python is a popular programming language. As of July 2019, Python is the 3rd most popular programming language in the world after Java and C according to the TIOBE (the software quality company) website.
The Python programming language has two versions, namely Python 2 and Python 3. The last update as of July 2019 for version 3, Python has released version 3.7.4. As for version 2, Python has released up to version 2.7.16. Python has many libraries that can be used to support program development, especially for data science programming. The libraries are as follows Numpy, SciPy, Pandas, Matplotlib, Seaborn, Bokeh, Plotly, SciKit-Learn, Theano, TensorFlow, Keras, NLTK, Gensim, Scrapy, Statsmodels, Flask.
3. RESULT AND DISCUSSION
To realize the research objectives and answer the problem formulations that have been set out in Chapter 1, the stages to be carried out in this research are described in Figure 4.
Figure 4. Flow Process Research The stages of the research as shown in Figure 3 are further explained as follows:
a. Identify the problem first, so that the solutions made can be used to solve the problems that are the topic of this research.
b. Conduct a study on the concept of the k-NN algorithm and presence system.
c. Conduct a more in-depth exploration of the k-NN algorithm.
d. Performing the preprocessing stage
e. Preparation of training data for attendance system.
f. Testing data testing 3.1 Program Flow Stages
The presence system with webcam using the k-nearest neighbor algorithm was built using the python 3.7.3 programming language which has the following program stages:
Figure 5. Program Flow Stages The following is an explanation of the stages of the program flow made:
a. Get data
b. The process of taking data or known as reading the data to be processed in the presence system.
c. Preprocessing include process check data, limiting k value, limiting training face data d. Data Training dan Data Test
At this stage is the process of sharing the data that will be processed, namely the existing data will be carried out data training and data testing. At this stage a comparison is made. For example, there are face data with a total of 10 people. Then the data for this test there are also 10 people. This is done to prevent overfitting and underfitting e. Application of KNN
At this stage, the k-NN algorithm will be calculated on the data that has been divided into train data sets and test data sets. For the selection of the value of k in the algorithm of k nearst neighbors using a trial approach.
f. Testing
After the data has gone through the calculation stage using k-NN, the next stage is the testing stage. The testing stage is the stage where existing data patterns will be tested using new test data.
g. Evaluation
The evaluation stage is the stage that aims to assess a presence system, so that in the future improvements can be made to the system. Usually the method used to evaluate a system is accuracy. Accuracy which has a definition as the level of closeness between the predicted value and the actual value.
a. Use lowercase letters and abjed for list numbering.
b. The 5 mm setting for the left protrudes inside.
c. If more than 1 numbering level uses numbering for the next list:
1. Use numbering.
2. Next 3.2 Research Data
One of the important factors in research is the data source, this happens because the data source will involve the quality of the research results. Therefore, the data source is a material consideration in determining the method of data collection. Data sources consist of two things, namely primary data sources and secondary data sources. And the data used in this study is only primary data.
a. Primary Data
Primary data is data obtained directly from the object of research, in this case the researcher obtains data or information directly by using research instruments that have been determined. Primary data were collected by researchers to answer questions about the research being carried out. Primary data collection is part of the research process and is necessary to achieve the objectives of decision making. In this study, the primary data directly used facial photos of PT Adroady employees.
b. Data Collection Techniques
For data collection using a purposive sampling approach. According to Hengki Wijaya (2019) purposive sampling is sampling based on certain considerations such as population characteristics or previously known characteristics. The sample in question is the facial data of PT Adroady employees. The following is a sample of PT Adroady's employee data. In this study, respondents were photographed with a camera with a maximum distance of 20 cm without considering the background of the sample data. The maximum image size of this study is 1 MB.
Respondents in this study will be taken with 3 pictures. The image is saved with the extension jpg or png. For this study, using facial poses without glasses and free to use any expression.
Samples of respondents' faces with several poses are shown in Figure follows.
Figure 6. Sample Face Respondence 3.2 Data Training and Testing
After exploring K-Nearsrt Neighbors in Chapter 2, the next step is to validate the training data. The training data validation stage is a stage that serves to test the presence system using test data, so that it can be seen how far this system is running and how much accuracy it produces. Validation of training data is carried out based on the stages of the attendance system program flow The following is the implementation of the stages of the program flow on training data validation:
a. Create a file structure like the following
1. Sistem Presensi : Application, Data, Train, Test, Model, __pycache__
Facialrecognizer.py __init__.py modeltrainer.py threaded_cv_capture.py train.py
utils.py 2. __pycache__
3. tmp
run.py
b. Doing class training data folder and test folder The initial stage is to create a training class folder and test data as follows with syntax in above
class DataDir:
DATA_DIR = ROOT_DIR + 'data' if not os.path.isdir(DATA_DIR):
os.mkdir(DATA_DIR)
TRAIN_DIR = DATA_DIR + '/train' if not os.path.isdir(TRAIN_DIR):
os.mkdir(TRAIN_DIR)
TRAIN_CLASS_FMT = TRAIN_DIR + '/{}' TRAIN_DATA_FMT = TRAIN_CLASS_FMT + '/{}' TEST_DIR = DATA_DIR + '/test'
if not os.path.isdir(TEST_DIR):
os.mkdir(TEST_DIR)
TEST_CLASS_FMT = TEST_DIR + '/{}' TEST_DATA_FMT = TEST_CLASS_FMT + '/{}' c. Create a training function
The stage of making this training function aims so that the facial data that will be carried out in the data training stage with the K-Nearest Neighbors algorithm can run well according with syntax in above
import math
from sklearn import neighbors import os
import os.path import pickle
import face_recognition
from face_recognition.face_recognition_cli import image_files_in_folder from datetime import datetime as dt
from . import TRAIN_DIR, MODEL_DIR d. Preprocessing Stage
This stage is carried out with an approach to limiting file formats that can be trained and tested by machine learning with the K-Nearst Neighbor algorithm. The file format used is in the init.py section
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
from .utils import ROOT_DIR, ModelDir, DataDir, generate_image_path from .threaded_cv_capture import ThreadedCVCapture
from .modeltrainer import ModelTrainer
from .facialrecognizer import ModelNotFound, Prediction, FacialRecognizer
e. Application of the K Nearest Neighbor Algorithm
The KNN process is in accordance with chapter 2.11, so the first step for implementing the K Nearest Neighbor algorithm is to call the appropriate library
from sklearn import neighbors
The next step after calling the K Nearest Neighbor library is to determine the value of k contained in the run.py script. Syntax like above
if action == 'train':
print("Training KNN classifier...")
trainer = ModelTrainer(n_neighbors=Insert value k) trainer.train()
print("Training complete!")
trainer.save(); print('Model saved!') trainer.update(); print('Model updated!') f. Testing
At this testing stage, a trial approach of the k value is carried out between the ranges of 1-3 to determine which k value is the best that will be used for the presence system. The testing code used for the prediction of the tested face
1. Testing value k = 1
The results of testing using a k value of 1 in the application of a face-based attendance system with the K Nearst Neighbor algorithm approach are shown in the following table :
Table 2. Prediction with k = 1
Name Acuration
Belo 96.51%
Rizki 98.82%
Jason 97.38%
Egha 98.37%
Amin 97.96%
Ocan 97.57%
Rosyid 92.96%
Andhika 99.15%
Sam 92.67%
Edi 94.78%
Edward 94.78%
2. Testing value k = 2
Table 3. Prediction with k = 2
Name Acuration
Belo 100%
Rizki 100%
Jason 100%
Egha 100%
Amin 96.18%
Ocan 95.17%
Rosyid 92.69%
Andhika 100%
Sam 92.67%
Edi 94.78%
Tidak diketahui 30.25%
3. Testing Value k =3
The results of testing using a k value of 3 in the application of a face-based attendance system with the K Nearst Neighbor algorithm approach are as follows
Table 4. Prediction with k = 3
Name Acuration
Belo 100%
Rizki 100%
Jason 96.06%
Tidak diketahui 30.25%
Egha 100%
Amin 96.18%
Ocan 95.97%
Rosyid 92.69%
Andhika 99.15%
Sam 92.67%
Edi 94.78%
4. CONCLUSION
From the results of several trials that have been carried out on the manufacture of attendance systems, the following conclusions is The selection of the k value has a large enough influence on the accuracy of the face classification for the presence system. So it is necessary to test the value of k repeatedly, until the best k value is found, In the test results of the best group data, the presence system using KNN succeeded in achieving an accuracy value of 100% and Training data that is not too large has an impact on program speed presence. It is necessary to add more training data.
Suggestions given for the development of a program recommendation system this study are Use algorithms with faster and more accurate processes, Facial recognition systems can be piloted in airport security systems or certain environments as a way to increase security and because the presence system created in this study does not yet have a better user interface, it is recommended for further recommendation system developers, to be able to use the user interface in the form of web-based or android-based applications. So it can be easier to use.
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