Haar Cascade Classifier Method Research Articles

SMART ATTENDANCE WITH FACE ANTI-SPOOFING TECHNOLOGY USING HAAR CASCADE CLASSIFIER

Traditional attendance systems often encounter challenges in efficiently and accurately recording attendance. This research aims to introduce an innovative solution through the development of an intelligent anti-spoofing attendance system based on facial recognition using the Haar Cascade Classifier method. Designed to overcome the inefficiencies in attendance recording, this system ensures the accuracy of educational staff attendance records. Its development method relies on the Haar Cascade Classifier, employing image processing to detect learned object features, particularly focusing on facial recognition. Research findings indicate that the implementation of this system achieves an average accuracy rate of 98.90% in attendance recording. The facial recognition technology ensures reliable attendance recording with confidence levels exceeding 80%, signifying precise facial identification that addresses various challenges and ensures attendance data integrity. Not only does the system identify educational staff with high accuracy, but it also provides prompt responses for efficient attendance logging and verification. Beyond its technical benefits, this study significantly contributes to the development of smarter and more efficient attendance technology. The system plays a crucial role in enhancing the discipline of educational staff at STMIK IKMI Cirebon and streamlining attendance management and evaluation across educational institutions.

APLIKASI PENGENALAN WAJAH UNTUK ABSENSI PADA SMA DUA MEI CIPUTAT MENGGUNAKAN METODE HAAR-CASCADE CLASSIFIER BERBASIS WEBSITE

The attendance application is one important system that must be possessed by a school in order to manage student attendance data. Some schools still use manual methods for student attendance by using signatures or calling them one by one. Manual attendance is considered inefficient because the risk of losing attendance sheets is very easy and parents cannot monitor whether their child is attending school or not. This is directly felt by SMA Dua Mei Ciputat with the presence of students who left home but did not attend school. Therefore, this research aims to assist teachers and parents in monitoring students to be more disciplined in coming to school by building a face recognition application for attendance using the haar-cascade classifier method based on a website. The haar-cascade classifier method has the advantages of computation in face detection and the local binary pattern histogram algorithm which has good accuracy in face recognition. The results of this research show that the haar-cascade classifier method can be used to create a face recognition application for attendance with fairly good accuracy, namely 100% with 5 attempts with recognition of one face, 100% with 5 attempts with recognition of two faces, and 20% with 5 attempts with recognition of three faces. With these results, the application is only capable of recognizing two faces simultaneously in the attendance process.

Implementation of Deep Learning in the Application of Lecture Attendance System with Face Recognition Technology Based on OpenCV


 
 
 OpenCV, as an object detection library, is employed as the foundation in the development of a facial recognition system. This system utilizes the Haar Cascade Classifier method embedded in OpenCV for facial detection, providing an efficient approach to identifying individuals. The research is conducted using the Python programming language. The initial stages involve a literature review, followed by data collection necessary for system training. System design incorporates the implementation of the Haar Cascade Classifier method from OpenCV, along with data analysis to comprehend and optimize facial recognition outcomes. After progressing through these stages, the system is tested to evaluate facial features that can reliably identify individuals. The experimental results are expected to offer valuable insights into the development of facial recognition technology using OpenCV, with potential applications across various domains.
 
 

Detecting Objects Using Haar Cascade for Human Counting Implemented in OpenMV

Sight is a fundamental sense for humans, and individuals with visual impairments often rely on assistance from others or tools that promote independence in performing various tasks. One crucial aspect of aiding visually impaired individuals involves the detection and counting of objects. This paper aims to develop a simulation tool designed to assist visually impaired individuals in detecting and counting human objects. The tool's implementation necessitates a synergy of both hardware and software components, with OpenMV serving as a central hardware device in this study. The research software was developed using the Haar Cascade Classifier algorithm. The research process commences with the acquisition of image data through the OpenMV camera. Subsequently, the image data undergoes several stages of processing, including the utilization of the Haar Cascade classifier method within the OpenMV framework. The resulting output consists of bounding boxes delineating the detection areas and the tally of identified human objects. The results of human object detection and counting using OpenMV exhibit an accuracy rate of 71%. Moreover, when applied to video footage, the OpenMV system yields a correct detection rate of 73% for counting human objects. In summary, this study presents a valuable tool that aids visually impaired individuals in the detection and counting of human objects, achieving commendable accuracy rates through the implementation of OpenMV and the Haar Cascade Classifier algorithm.

“Tec-House” WEBCAM-BASED REMOTE SENSING SYSTEM FOR HOME AND BUILDING SECURITY USING THE HAAR CASCADE METHOD

A home security system is something that every home owner must pay attention crimes such as burglary often put homeowners at risk. Therefore we need a tool that can bring together automatically remotely to protect the house. The system worked on in this article is a remote sensing system based on webcam. The method used in this sensing system uses the haar cascade classifier method. The results obtained from this remote sensing system are for the implementation of the system on homeowner data sets with 98% results, while for non-home owner image data sets with 96% results. From the results of using a webcam-based remote sensing system using the Haar Cascade Classifier method it can be implemented properly and the average error is 97%. The existence of this Tec-House tool can reduce the crime of theft in a house or building.

Sign Language Translator Based on Raspberry Pi Camera Using The Haar Cascade Classifier Method

Sign language is the main tool of communication for people with hearing impairments. Communication is very limited and difficult to understand between normal people who do not know sign language, so an interpreter is needed. Where not everyone, even a few normal people, learns sign language, especially the Indonesian Sign Language System (SIBI). Motion Detection is an important subject in the field of computer vision, which is used by many systems. Today's Internet of Things is very helpful and facilitates daily human activities. An internet network allows a device to be controlled from a considerable distance. This study described a sign language translator tool for the deaf and speech impaired using a raspberry-pi camera and displayed it on the other device monitor. This system was built using the Python programming language and the OpenCV Library. The system is using Haar Cascade Classifier algorithm, where there will be data on all hand shapes based on the letters to be translated. This application uses the OpenCV library and Visual Studio Code IDE software connected to the Raspberry Pi Camera. The publisher will send data to other devices using the MQTT Broker to connect and display detection results to other device monitors wirelessly using a local network. The research was conducted at various distances between the hand and the webcam, from 30cm to 150cm. The research results using the Haar Cascade Classifier method to detect sign language obtained an accuracy of 82%.

IMPROVED FACE DETECTION ACCURACY USING HAAR CASCADE CLASSIFIER METHOD AND ESP32-CAM FOR IOT-BASED HOME DOOR SECURITY

Some people are very easy to open the door lock with just a small wire. This causes the house to be vulnerable to burglary and theft. In previous studies, there were still shortcomings such as the accuracy of facial recognition was not good, the time for the facial recognition process was very long and no action was taken if the camera caught an unknown person. This raises the need for solutions related to security systems that can monitor homes when something suspicious happens so that it can be prevented immediately. This study aims to create a home door security system using ESP32-CAM as face recognition. This face recognition can unlock the door automatically and if someone is caught on camera who is not known, the system will send a notification to the owner to follow up on this. The results of the face detection test using the Haar Cascade Classifier method that can distinguish a known face and an unknown face. The results of facial accuracy at a distance of 30 cm, 40 cm, and 50 cm with a light intensity of 130 lux obtained an average accuracy of 96.6%. The result of the average time required from the face sampling process until the face is recognized by the system is 21,50 seconds.

Machine Learning Techniques for Vehicle Detection

The traffic surveillance system is a type of intelligent system of traffic control. Traffic control provides solutions to most problems faced by people. It helps to monitor, detect traffic congestion and traffic accidents. As science evolved, it became possible to control traffic using video surveillance. Video surveillance is the most economical option that does not involve high costs or changes in infrastructure. Vehicle detection is one of the main parts of the traffic surveillance system. In this paper, vehicles will be detected using two different artificial intelligence methods (the YOLO method and the HAAR cascade classifier method). The first one is smarter than the second method, and both of them contain machine learning. The first processing step will read the video. Then vehicle detection algorithms are applied using two different ways. The comparison between them depends on the results to find the most effective and applicable vehicle detection method. After implementing the two methods, results were obtained using YOLO, that the accuracy is 91.31% and the error rate is 8.69%, in time 10 sec. As for using the XML (HAAR cascade classifier method) method, the accuracy is 86.9%, the quality is 86.9%, completeness is 90.9%, and the error rate is 13%, in time 17 sec. Thus, we conclude that the YOLO method has better results than the second method. Index Terms— Artificial Intelligence, Machine Learning, Vehicle Detection, Yolo Method.

Low Cost System for Face Mask Detection Based Haar Cascade Classifier Method

In December 2019, there was a pandemic caused by a new type of coronavirus, namely SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2) spread almost throughout the world. The World Health Organization (WHO) named it COVID-19 (Coronavirus Disease). To minimize the spread of the COVID-19, the Indonesian government announced a policy for the social distancing of 1-2 meters and wearing a medical mask. In this study, a mask detection system was built using the Haar Cascade Classifier method by detecting the facial areas such as the nose and lips. The study aims to distinguish between using masks and on the contrary. It is expected that the mask detection system can be implemented to provide direct warnings to people who do not wear masks in public areas. The results using the Haar Cascade Classifier method show that the system designed is able to detect faces, noses, and lips at a light intensity of 80-140 lux. The face is detected at a distance of 30-120cm, while the nose is at a distance of 30-60cm, while the lips are at a distance of 30-70cm. The system designed can perform the detection process at a speed of 5 fps. The overall test results obtained a success rate of 88,89%.

RANCANG BANGUN APLIKASI NEW NORMAL COVID-19 DETEKSI PENGGUNAAN MASKER MENGGUNAKAN HAAR CASCADE CLASSIFIER

During the COVID-19 pandemic, when in public places, it is required to apply the 4M health protocol, namely wearing masks, washing hands, maintaining distance, and avoiding crowds. In its implementation, there are officers who always maintain and remind people not to violate health protocols. Like remembering to wear a mask. The mask detection application is made as a computerized surveillance system that can store images of violations of the use of masks and provide warning sounds. Observations, discussions and literature studies are sources of data in this empirical research. Using Python as a programming language assisted with OpenCV for image processing. After passing through the 4 stages of Waterfall, namely Analysis, Design, Manufacturing and Development and Testing, an application is produced where the Raspberry Pi is a processing tool and images are captured from the camera module with a resolution of 1080x1024 px. This application can detect the use of masks with an accuracy of 90.5% using the Machine Learning Haar Cascade Classifier method. Where the condition of the face is a maximum of 30 degrees turned to the side and looked up

Design of printed circuit board detection with image information technology

In this research, an automatic PCB’s deffects checking tool will be made with image processing technology that adopts Haar Cascade Classifier method as a decision maker for whether or not a broken circuit on the PCB, with this technology the PCB will be detected using a square feature to show the broken path. will be displayed on the interface on the monitor. From the results of the test, data using the Haar Casscade Classifier method has the average percentage of successful detection of broken paths on the PCB at a camera distance of 26 cm and 16 cm is 52.49% with the maximum hassle obtained when the camera distance is placed at a distance of 26 cm and an angle position of 105 degrees which has a success rate of 97.5%. This method is good enough for PCB defect detection tools.

Sistem Pencatatan Kehadiran Deteksi Wajah Menggunakan Metode Haar Feature Cascade Classifier

Biometric innovation is one source that can be used in security frameworks such as facial recognition as personal information. The human face has a lot of data and has the most normal and widely used attributes for the presentation of human character. As well as communicating feelings and considerations, faces can also be used to identify individuals. One of the innovations in the recognition statement applied to biometrics is the use of the human face as a recording system in the field of education. However, there are several circumstances related to the recording of student absenteeism currently occurring in the field of education, namely " Titip Absent " . Therefore, in this study proposed a system that uses a student attendance recording system using computer technology to reduce the level of cheating when filling in attendance forms and the effectiveness of student data processing, the Haar Cascade classification method is used to record the student attendance process using a biometric system. The algorithm applied in the Haar Cascade classifier method uses a face detector called the "Cascade Classifier". The result of this study is an attendance application that can detect whether all users who have recorded attendance have been registered in the system with the distance between the face and the camera is 50 cm with an accuracy rate of 70%.

THE CALCULATION SYSTEM OF NUMBER OF PEOPLE IN A ROOM BASED ON HUMAN DETECTION USING HAAR-CASCADE CLASSIFIER

Data of number of people in the room, calculations are usually carried out by assigning someone to oversee a room. In this final project, a system for calculating the number of people in the room is designed with image processing based on human detection that can be used in rooms, both for commercial applications and for security. This system uses Raspberry Pi device that already has an image processing method Haar-Cascade Classifier. Input data is in the form of video taken directly via webcam to be captured into a frame so that it can be used as a input the Haar-Cascade Classifier method and perform the counting process will be sent to the Antares platform. The system design has been tested with five scenarios. Scenario 1 the effect of the distance of the object, scenario 2 the effect of the pose of the object, scenario 3 the effect of the amount the object in the frame, scenario 4 affects the scale factor and scenario 5 measurement computation time. Scenarios 1 to 3 will do the best configuration for minimum neighbour. The system gets the best accuracy of 98,5% when the object distance 4 meters, the best accuracy of 96,6% when the object is facing forward and accuracy the best is 97,7% when the object in the frame is more than two objects with the best configuration use the minimum neighbour 5. Scenario 4 gets accuracy the best is 76,2% when using the scale factor 1.1. Scenario 5 gets the average computation time of the system is under one second, meaning the detection process done pretty fast.

Traffic Light Automation with Camera Tracker and Microphone to Recognize Ambulance Using the HAAR Cascade Classifier Method

Lack of knowledge by road users regarding these priorities, especially when there is a passing ambulance that is often stuck in traffic at a crossroads due to accumulated vehicles and the traffic light is still red. The purpose of this paper is to simulate traffic light automation by giving a green light every time an ambulance passes by using the HAAR and Computer Vision methods. The HAAR method is used for training data from less sharp images as part of the Ambulance object classification process. The Computer Vision method is used as a tool in image processing objects to processing the image captured by the Camera. Hardware through the microphone performs pattern recognition to pick up ambulance sirens. The test result at the average frequency caught by the microphone is 1.3 kHz. The test results of the System to capture ambulance objects received a precision value of 75%, a recall of 100%, and an accuracy of 75%.

Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi.

Background:Drowsiness condition is one of the significant factors often encountered when an accident occurs. We aimed to detect a method to prevent accidents caused by drowsiness and lost a focused driver.Methods:The image processing technique has been capable of detecting the characteristic of drowsiness and lost focus driver in real-time using Raspberry Pi. Video samples were processed using the Haar Cascade Classifier method to identify areas of the face, eyes, and mouth so that drowsy conditions. The methods can be determined based on the bject detected.Results:Two parameters were determined, the lost focused and drowsiness driver. The highest accuracy value for driver lost focused detection was 88.00%, while the highest accuracy value for drowsiness driver detection was 90.40%.Conclusion:In general, a system developed with image processing methods has been able to monitor the drowsiness and lost focused drivers with high accuracy. This system still needs improvements to increase performance.

Kontrol Presentasi Telapak Tangan Menggunakan Haar Cascade Classification

Presentasi yaitu suatu kegiatan yang dilakukan sebagai penyampaian ilmu oleh pengajar untuk memberitahukan hasil penelitian serta gagasan. Presentasi sudah menjadi aktivitas yang dilakukan di hampir semua bidang pekerjaan sehari-hari.Untuk melakukan presentasi, dibutuhkan sebuahkomputer, mouse, dan keyboard sebagai alat bantu dan sarana untuk menjalankan slide presentasi. Selain dibutuhkan alat bantu presentasi, saat presentasi juga dibutuhkan seseorang operator untuk mengendalikan komputer saat presentasi berlangsung. Dengan melihat kondisi tersebut, dibutuhkan teknologi yang dapat mengintegrasikan berbagai macam fungsi alat bantu presentasi dan pengendali komputer saat presentasi. Deteksi telapak tangan pada penelitian ini memakai bahasa pemrograman python dan juga sebuah library OpenCV untuk proses inputan. Metode yang digunakan untuk mendeteksi gerakan telapak tangan menggunakan metode Haar Cascade Classifier. Metode Haar Cascade Classifier dapat digunakan untuk mendeteksi objek telapak tangan dan mampu mengontrol slide dengantelapak tangan dengan webcam secara real time. Dalam pendeteksian harus ditentukan centroid agar dapat diketahui jumlah frame untuk memindahkan slide serta jarak dari webcam ke objek juga dapat mempengaruhi dalam mengontrol presentasi. Namun, sistem ini memiliki banyak kelemahan dan harus diperbaiki. Sensitifitas pendeteksian yang tidak terlalu baik yang dapat dipengaruhi oleh beberapa faktor yaitu webcam yang kurang bagus, spesifikasi Personal Computer (PC) rendah, dataset yang meyebabkan false negative, serta pencahayaan ataupun pengaruh dari background.

Pengenalan Personal Menggunakan Citra Tampak Atas pada Lingkungan Cashierless Strore

Personal recognition with image processing techniques from the side view has the disadvantage of being applied to the cashierless store environment, namely inaccurate recognition or identification when personal collisions occur. To overcome this, the image capture method is used from the top-view. Personal recognition method through the top-view image using the Haar Cascade Classifier method. 1420 positive images and 2170 negative images are used to find features that are considered suitable for recognizing objects using the Adaptive Boosting (Adaboost) method. Tests were carried out on 100 test data by varying the parameters of min_neighbors (3.4, and 5) and the size of the dataset window (25x25, 35x35, 45x45 pixels). Personal recognition testing gets the highest accuracy of 89.9% with the parameters used are min_neighbors 5 and the size of the 25x25 pixel dataset in the detection parameter size of min_size 140x140 pixels.

Menuju Pengenalan Ekspresi Mikro: Pendeteksian Komponen Wajah Menggunakan Discriminative Response Map Fitting

The observations made in the study of micro-expression are to recognize and track the very subtle movements of certain facial areas and in a short time. In this study, the observation of movement is held in some areas of the face component. The facial and facial components detection is the pre-process stage on micro-expression recognition system. The goal at this stage is to get face and face components accurately and quickly on every movement of the video sequence or image sequence. The face landmark point of the Discriminative Response Map Fitting (DRMF) method can be used to get face components area accurately and quickly. This can be done because the facial landmark points used in this model-based method do not change when objects are moved, rotated, or scaled. The results obtained by using this method are accurate with a 100% accuracy value compared to the Haar Cascade Classifier method with an average accuracy of 44%. In addition, the average time required in the formation of facial component boxes for each frame is 0.08 seconds, faster than the Haar Cascade Classifier method of 0.32 seconds. With the results obtained, then the detection of facial components can be obtained accurately and quickly. Furthermore, the boxes of face components obtained are expected to display the appropriate data to be processed correctly and accurately in the next stage, feature extraction and the classification of micro-expression motion stage.

DETEKSI KEDIPAN MATA PADA VIDEO MENGGUNAKAN OPEN CV

In the development of technology in the current era is one of the most rapid progress, it is marked by almost all managers of data and information has been done with the computer because of the increasingly diverse information issues handled, Detection of objects is one of the most important early stages before Done object recognition process. Eye blink detection can be used to open a new file or an application. In this research use case detection eye blinking on video using OpenCV to open notepad application. The blink detection uses the haar cascade classifier method for realtime detection and the OpenCV Library. The first step determines the pattern 1111100000. Next enter the video sample avi format to match with a predetermined pattern. In this program the system managed to detect the eye according to the pattern by circling the eye area when the eyes are open or literate and when the eyes closed or brake there is no circle in the eye area for the correct video sample.

Pengolahan Citra Digital untuk Keyboard Virtual Sebagai Antarmuka pada Aplikasi Berbasis Web

AbstrakSejak pertama kali komputer ditemukan, keyboard selalu menjadi alat utama yang menjadi penghubung interaksi antara manusia dan komputer. Saat ini banyak komputer yang menerapkan teknologi pengolahan citra untuk menjadikannya perantara interaksi antara komputer dan manusia.Dalam penelitian ini, penulis mencoba untuk menerapkan teknologi pengolahan citra yang digunakan untuk keyboard virtual pada aplikasi web. Digunakan webcam untuk menangkap citra ujung jari telunjuk. Hasil capture citra akan dikirimkan ke server localhost untuk diproses dengan image processing. Untuk mendeteksi ujung jari telunjuk, digunakan metode Haar Cascade Classifier. Proses pendeteksian tersebut menghasilkan koordinat yang akan dikirimkan ke aplikasi web yang selanjutnya dijadikan acuan untuk menentukan posisi tombol pada keyboard virtual. Sehingga keyboard virtual akan menampilan karakter sesuai dengan yang ditunjuk oleh ujung jari telunjuk.Dari hasil pengujian yang dilakukan, jarak optimal ujung jari telunjuk dengan webcam adalah 20 – 35 cm. Derajat kemiringan ujung jari telunjuk untuk dapat terdeteksi antara 0° – 10°. Sistem mampu mengenali ujung jari telunjuk pada ruangan berlatar belakang putih polos dan terdapat sedikit perabot. Waktu respon untuk menampilkan karakter keyboard virtual rata-rata 5,156 detik. Sehingga keyboard virtual pada sistem ini belum mampu dijadikan antarmuka pada aplikasi web, dikarenakan masih sulit digunakan dalam mengarahkan ujung jari telunjuk ke tombol karakter yang diinginkan.Kata kunci—aplikasi web, Haar Cascade Classifier, keyboard virtual, pengolahan citra AbstractSince the first computer was founded, keyboard is always been a primary tool for interaction between humans and computers. Today, many computers use image processing technology to make interaction between computers and humans.The author try to apply image processing technology that implemented to virtual keyboard on web application. Using a webcam to capture the tip of index finger and the results will be sent to the localhost server for processing with image processing. Using Haar Cascade Classifier method to detect the tip of index finger, it will produce coordinates that sent to the web application and it used as a reference for determining button positions on virtual keyboard. Virtual keyboard characters will display after appointed by the tip of index finger.From testing results, optimal distance from index finger to webcam is 20 – 35 cm. System can recognize the tip of index finger on white background and room with few furnitures. Average response time for displaying virtual keyboard sentences is 3 minutes and 28.838 seconds. So the virtual keyboard on this system was not able to be used as interface on web application, because it difficult to use in directing the tip of index finger to the character keys.Keywords—web application, Haar Cascade Classifier, virtual keyboard, image processing