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Alfons, Billy Cornelio, and Robertus Setiawan Aji Nugroho. "DIRECT DETECTION OF PEOPLE WEARING GLASSES USING THE HAARCASCADE CLASSIFIER." Proxies : Jurnal Informatika 4, no. 1 (2024): 47–73. http://dx.doi.org/10.24167/proxies.v4i1.12435.
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In this day and age, many people wear glasses to help their eyesight or to add style to make them look more attractive. In some places it is mandatory for someone not to wear glasses for certain reasons such as someone who goes to an ATM machine not to wear dark colored glasses so that his face can be seen clearly and is better known for security reasons. In this situation it is very important for some places to be able to detect a person wearing glasses using the camera directly for some reason in order to quickly recognize the person's face more clearly. The haarcascade classification algorithm is an algorithm that can detect faces and eyes directly and quickly using a camera connected to a computer. The haarcascade that I use is the frontalface haarcascade to detect faces and the eye haarcascade to detect the eyes, and the results of the detection if the face and eyes are detected, it is certain that someone is not wearing glasses and vice versa if only a face is detected, it is certain that someone is wearing glasses. OpenCV to insert live video and processed by the library that we use. The final result that we get in this project is an image that has been captured and has been run through a dataset, namely direct video input that has been processed using haarcascade frontalface and haarcascade eye and opencv. At the top of a person's face there will be a text that explains whether the person is wearing glasses or not, and can count the number of faces and eyes of a person recorded on the camera.
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Vikram Bhattacharya, Aditya, Mrinalini Khanna, Akshay Tripathi, and S. Murugaveni. "Class Monitoring System Tools MTCNN and Haarcascade Classifier." International Journal of Engineering & Technology 7, no. 3.12 (2018): 951. http://dx.doi.org/10.14419/ijet.v7i3.12.17609.
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The project aims towards the assistance of teachers at the time of taking attendance. The system solely focuses on face detection and recognition. The tools used to device the system are API’s offered by Python 3.6, Open CV(for detection) and a few cognitive tools provided by Azure.The basic idea behind the project is face recognition linked to a database backend. The information of the student attending the class is stored here. The entire attendance is associated with two types of time stamps incorporated at the server end. The time stamp helps to keep a track of the hour conducted and the time for which number of people attended the class. Exceptions in the time stamp would be incorporated in order to cater for the students leaving the class or trying to bunk the class. In case of further exceptionsin the time stamp will be scope of further development of the system. All queries or conditions of the students will be answered by the system on communication with the admin. If the admin finds that the system was at fault then it can always be fixed by the admin for smooth functioning of the class monitoring system.
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Farhan Ramadhan, Haikal, Kana Saputra S, Said Iskandar Al Idrus, Zulfahmi Indra, and Insan Taufik. "IMPLEMENTASI METODE HAARCASCADE CLASSIFIER DALAM MENGIDENTIFIKASI OBJEK WAJAH MANUSIA." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 4 (2025): 6729–35. https://doi.org/10.36040/jati.v9i4.14143.
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Keamanan menjadi aspek esensial dalam berbagai sektor, terutama pada era teknologi modern yang menuntut sistem pengamanan canggih. Salah satu inovasi dalam identifikasi dan autentikasi adalah pengenalan wajah, metode yang andal, tidak invasif, dan sesuai berbagai konteks. Dalam penelitian ini, algoritma Haar Cascade Classifier dan arsitektur jaringan saraf Inception V3 digunakan untuk meningkatkan efisiensi serta akurasi pengenalan wajah. Penelitian ini merespons tiga permasalahan utama, yaitu kebutuhan sistem keamanan modern, kendala akurasi, dan keandalan teknologi pengenalan wajah saat ini. Penelitian bertujuan menganalisis kemampuan dan kinerja Haar Cascade Classifier dalam mendeteksi wajah dengan variasi pose, serta mengoptimalkan implementasinya untuk memenuhi kebutuhan aplikasi dunia nyata. Metodologi meliputi pengumpulan data berupa citra wajah, pra-pemrosesan data (normalisasi piksel, augmentasi data, segmentasi wajah), serta pemisahan dataset untuk pelatihan (80%) dan pengujian (20%). Proses implementasi melibatkan Haar Cascade Classifier untuk deteksi wajah dan arsitektur Inception V3 untuk pengenalan wajah. Hasil evaluasi menunjukkan bahwa dengan learning rate 10e-4, model mencapai akurasi 100%, jauh lebih tinggi dibandingkan learning rate 10e-6 yang hanya mencapai akurasi 56%.
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Putra, I. Nyoman Tri Anindia, and Evi Dwi Krisna. "Implementasi Sistem Surveillance Berbasis Pengenalan Wajah pada STMIK STIKOM Indonesia." Jurnal Ilmu Komputer 13, no. 2 (2020): 8. http://dx.doi.org/10.24843/jik.2020.v13.i02.p01.
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Sistem pengenalan wajah manusia merupakan salah satu bidang yang cukup berkembang dewasa ini, dimana aplikasi dapat diterapkan dalam bidang keamanan (security system) seperti ijin akses masuk ruangan, pengawasan lokasi (surveillance), maupun pencarian identitas individu pada database kepolisian. Penelitian ini bermaksud untuk merancang bangun sistem surveillance berbasis pengenalan wajah dengan menggunakan metode pengenalan wajah yang berfokus pada jarak dan akurasi dari pendeteksian wajah melalui kamera yang dapat dikenali melalui metode haarcascade classifier. Hasil dari penelitian dengan menggunakan metode tersebut memperoleh rata rata deteksi wajah dengan jarak maksimal adalah 3 meter dari kamera. Sedangkan hasil untuk akurasi pengenalan wajah dengan metode eigenface dan haarcascade classifier masing masing memperoleh akurasi 1 meter sebesar 87%, 2 meter sebesar 83%, dan 3 meter sebesar 63%.
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Ramadan, Fadli, Muhammad Fatchan, and Tri Ngudi Wiyatno. "APLIKASI SISTEM PENGENALAN WAJAH UNTUK VERIFIKASI MAHASISWA DAN PENGAWASAN DI LINGKUNGAN KAMPUS." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 3 (2025): 4256–58. https://doi.org/10.36040/jati.v9i3.13609.
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Manusia memiliki kemampuan yang sangat mudah dalam mengenali objek, berbeda dengan komputer yang memerlukan proses pelatihan yang panjang untuk dapat mengenali suatu objek. Terdapat berbagai metode yang dapat digunakan untuk melatih komputer agar mampu mendeteksi objek secara akurat, salah satunya adalah algoritma Haarcascade Classifier. Penelitian ini berfokus pada deteksi wajah menggunakan algoritma tersebut. Haarcascade Classifier merupakan algoritma yang umum digunakan dalam pendeteksian wajah. Dengan algoritma ini, sistem komputer dapat dilatih untuk mengenali citra wajah. Proses pelatihan membutuhkan dataset yang terdiri dari gambar wajah. Setelah pelatihan selesai, sistem yang dihasilkan mampu mendeteksi wajah secara efektif. Pada penelitian ini digunakan OpenCV yang terhubung dengan kamera eksternal XWF-1080P untuk menjalankan sistem deteksi wajah secara langsung dan Flask untuk mengimplementasikan aplikasi ke dalam bentuk web. Hasil pengujian menunjukkan bahwa sistem berhasil mendeteksi wajah dengan akurasi 95,6% pada 114 citra uji, dengan waktu respons rata-rata 0,8 detik per deteksi. Aplikasi berbasis web ini juga memberikan tampilan yang intuitif, memudahkan pengguna dalam melakukan verifikasi wajah.
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K, Dayana. "Surveillance Robotic Car." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 403–8. http://dx.doi.org/10.22214/ijraset.2024.63115.
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Abstract: Surveillance robotic cars, equipped with a myriad of modules, cameras, and communication systems, traverse diverse environments, providing real time data for surveillance purposes. These vehicles, equipped with PiRGB arrays and high resolution cameras, capture detailed, real time imagery for effective threat detection. Using Haarcascade Classifier and OpenCV, they track human faces. Algorithms like Pigpio, Haarcascade, and Local Binary Patterns Histograms helps in identifying face detection and motion tracking, while GPS Neo 6M, GSM SIM 800L, HC-05 Bluetooth module, and Arduino Uno ensure accurate location tracking of the vehicle. Hardware such as the Arduino Uno and Motor Driver L293D supports precise movement of the vehicle. The metal detector, paired with a buzzer, identifies metallic objects present on the navigation path of the vehicle. These surveillance robotic cars can be deployed in various environments, from urban areas to dense forests.
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Masnur, Masnur, Syahirun Alam, Muhammad Zainal, and Muhammad Emil Fazil. "PERANCANGAN SISTEM PENGENALAN WAJAH MENGGUNAKAN PYTHON, OPENCV DAN HAARCASCADE." Jurnal INSTEK (Informatika Sains dan Teknologi) 9, no. 2 (2024): 285–98. https://doi.org/10.24252/instek.v9i2.50354.
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Teknologi pengenalan wajah telah menjadi solusi populer dalam meningkatkan keamanan dan efisiensi akses di berbagai institusi, termasuk perpustakaan, namun keterbatasan anggaran dan infrastruktur di institusi pendidikan sering kali menjadi kendala dalam implementasi sistem yang efektif. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem pengenalan wajah berbasis Python, OpenCV, dan Haarcascade di Perpustakaan Universitas Muhammadiyah Parepare sebagai solusi yang terjangkau dan efisien untuk manajemen akses pengguna. Metode yang digunakan meliputi Haarcascade untuk mendeteksi wajah dengan mengidentifikasi fitur terang-gelap wajah melalui cascade classifier, Python sebagai bahasa pemrograman utama untuk mengintegrasikan dan menjalankan algoritma pengenalan wajah, dan Jupyter Notebook sebagai platform pengembangan untuk memfasilitasi pemrograman serta dokumentasi visual dari seluruh proses. Pengujian dilakukan dalam kondisi lingkungan perpustakaan yang bervariasi, termasuk perubahan pencahayaan dan sudut pandang wajah. Hasil penelitian menunjukkan bahwa sistem mampu mendeteksi wajah dengan tingkat akurasi tinggi, respons cepat, dan tingkat false positives yang rendah, sehingga cocok untuk kebutuhan perpustakaan yang memerlukan manajemen akses yang otomatis dan efektif. Implikasi dari penelitian ini adalah sistem ini memberikan solusi yang tidak hanya hemat biaya tetapi juga dapat diandalkan dalam kondisi terbatas, memberikan kontribusi bagi literatur pengenalan wajah dalam lingkungan pendidikan dengan sumber daya yang terbatas. Hasil ini menunjukkan bahwa sistem pengenalan wajah berbasis Haarcascade, Python, dan Jupyter Notebook dapat diadaptasi untuk aplikasi lain di institusi pendidikan, khususnya yang memerlukan solusi keamanan berbasis teknologi yang efisien.
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Gunawan, Lukman Syahrul, Busro Akramul Umam, and Masdukil Makruf. "Indonesia Rancang bangun aplikasi absensi mahasiswa menggunakan metode Viola Jhones algoritma HaarCascade di UIM." Energy - Jurnal Ilmiah Ilmu-Ilmu Teknik 13, no. 1 (2023): 8–15. http://dx.doi.org/10.51747/energy.v13i1.1052.
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Pengenalan citra wajah manusia merupakan salah satu teknologi utama yang terus dikembangkan. Di bidang Computer Vision dalam penerapannya dalam sistem pengenalan biomatrik, Sistem pencarian, pengindeksan pada database video digital, sistem keamanan kontrol akses area terbatas, konferensi video, interaksi manusia dengan komputer. dan lain sebagainya. Metode Viola-Jones adalah metode deteksi objek yang memiliki akurasi yang cukup tinggi yaitu sekitar 93,7% dengan kecepatan 15 kali lebih cepat dari detektor Rowley Baluja-Kanade dan sekitar 600 kali lebih cepat dari detektor Schneiderman-Kanade. Algoritma Haar Cascade Classifier adalah salah satu algoritma yang digunakan untuk mendeteksi sebuah wajah. Cascade Classifier digunakan dalam mendata absensi dengan pengenalan wajah yang dapat mendata mahasiswa secara real-time .
 Kata Kunci: Deteksi wajah, OpenCV, Haar Cascade
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Nicrocia, Teffo Phomolo, Owolawi Pius Adewale, and Pholo Moanda Diana. "Clustering an African Hairstyle Dataset using PCA and K-Means." International Journal on Cybernetics & Informatics 12, no. 3 (2023): 55–65. http://dx.doi.org/10.5121/ijci.2023.120305.
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The adoption of digital transformation was not expressed in building an African face shape classifier. In this paper, an approach is presented that uses k-means to classify African women images. African women rely on beauty standards recommendations, personal preference, or the newest trends in hairstyles to decide on the appropriate hairstyle for them. In this paper, an approach is presented that uses K-means clustering to classify African women's images. In order to identify potential facial clusters, Haarcascade is used for feature-based training, and K-means clustering is applied for image classification.
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R, SARAVANAN,. "FACIAL EMOTION RECOGNITION SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34371.
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Face Emotion recognition play a significance role in fields like aid, border management, police work, banking services, and client product. Facial expressions is wide utilized in social communication since they convey heaps of knowledge regarding folks, like moods, emotions, and alternative things. during this paper, we tend to review facial feeling recognition victimisation CNNs and highlight totally different algorithms and their performance impact. Further, we tend to demonstrate that utilizing CNNs during this field - ends up in a considerable performance increase. By forming associate ensemble of recent deep CNNs, we tend to get a FER2013 take a look at accuracy of 91.2%, outperforming previous works while not requiring auxiliary coaching knowledge or face registration. Key Words: Facial Expression, Confusion Matrix, Emotion Optimizer, Haarcascade classifier
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Budiman, Bunardi, Chairisni Lubis, and Novario Jaya Perdana. "PENDETEKSIAN PENGGUNAAN MASKER WAJAH DENGAN METODE CONVOLUTIONAL NEURAL NETWORK." Jurnal Ilmu Komputer dan Sistem Informasi 9, no. 1 (2021): 40. http://dx.doi.org/10.24912/jiksi.v9i1.11556.
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“Face Mask Detection Using the Convolutional Neural Network” is a PC based program that aims to detect and classify human beings whether a person is using a mask or not with access through a webcam camera. This program is created using the Python language with several libraries. The classification of face masks uses the Convolutional Neural Network method with the MobileNetV2 architecture. Meanwhile, human face detection uses the Haarcascade Classifier. How the program works is by accessing the connected camera and if the person detected is wearing a mask, the person will be labeled "using a mask" and given a green box to mark the detection along with the analysis value, whereas if not, it will be labeled "not using a mask" and a red box with also the predicted value. From the test results, it can be proven that the accuracy program is good enough to detect the use of face masks with an average object detection accuracy of 88.53% and the classifier for the use of mask an average of 84.45%.
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Nandiwale, Rajashree. "Review Paper on Face Recognition-based Attendance System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41701.
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The Attendance System performs image and class training that enables OpenCV data extraction functionality. The central objective behind this project involves building Face Recognition technology for attendance management to transform existing manual procedures into new automated systems. The system operates within the classroom space to train student with information that consists of name along with roll number and class details and sections and images. The extraction of images occurred through OpenCV software. When the corresponding class period began students would approach the machine for a photo capture session against the registered database photos. The development of a facial recognition-based attendance solution through Raspberry Pi forms the main objective of this project. The integration of face recognition algorithms in this system removes the requirement for manual user contact and achieves better precision levels together with enhanced dependability. The system first recognizes faces through its capture function and then creates records about attendance while determining presence as well as absence based on time spent in front of the system. The application features Face Detection and Face Recognition functionalities conducted through the Haar Cascade classifier using Open CV algorithms executed on Raspberry Pi hardware. Key Words: Face Detection, Face Recognition, HaarCascade classifier, Open CV, Raspberry Pi
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Effendi, Muhammad Ridwan. "SISTEM DETEKSI WAJAH JENIS KUCING DENGAN IMAGE CLASSIFICATION MENGGUNAKAN OPENCV." Jurnal Teknologi Informatika dan Komputer 4, no. 1 (2018): 27–35. http://dx.doi.org/10.37012/jtik.v4i1.283.
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Banyaknya ras kucing yang diakui secarajenis klasifikasi diantaranya seperti Anggora, Persia,kucing local dan lain lain. Setiap ras memiliki cirikhusus, maka penentuan ras kucing asli menjadi sulit.Dikarenakan kurangnya informasi tentangmembedakan klasifikasi jenis kucing. Maka penulismenganalisa identifikasi dan mengenali jenis kucingAnggora dan Persia melalui bentuk hidung dan wajahdan kaki yang dapat diambil melalui image denganmenggunakan OpenCV (Open Source ComputerVision Library). Metode penelitian berupapengamatan langsung, pengumpulan data objek, danstudi pustaka (Library Research) denganmenggunakan perangkat lunak Visual Studio danLibrary OpenCV dan Python. Dalam menggunakanmetode image classification karena klasifikasi citramerupakan suatu proses pengelompokan seluruhpixel pada suatu citra kedalam kelompok sehinggadapat diinterpretasikan sebagai suatu property yangspesifik. Metode image Classification menggunakandua algoritma yaitu Haarcascade dan Viola Jones.Haarcascade yaitu Haar Classifier merupakanpengklasifikasian fitur yang digunakan dalam metodeviola jones. Viola Jones merupakan metode yangpaling banyak digunakan untuk mendeteksi objek halini dikarenakan viola jones memiliki algoritma yangefisien. Hasil analisa riset penulis pada pendeteksiangambar menggunakan Python dan untukmenampilkan image dengan menggunakan opencv.
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Loginov, Vladislav, Kirill Shchipanov, and Vladislav Lavrov. "DEVELOPMENT OF WEB APPLICATION SYSTEMS IDENTIFICATION OF METAL DEFECTSON HALF-TONE IMAGES USING THE HAARCASCADE CLASSIFIER ON ASP.NET CORE MVC PLATFORM." Cherepovets State University Bulletin 4, no. 91 (2019): 7–20. http://dx.doi.org/10.23859/1994-0637-2019-4-91-1.
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Singh, Kripanshu, Manan Rastogi, Mimansa Mahajan, and Dr Ranjeet Kumar. "Accident Prevention by Detecting Drivers Fatigueness." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3919–24. http://dx.doi.org/10.22214/ijraset.2022.43275.
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Abstract: Various studies indicate that fatigueness in drivers leads to road accidents. It can lead to serious injuries like brain damage or it can lead to death. Therefore preventing people from harming the countermeasure device is necessary as a reliable solution. This study therefore came up with a new way of explaining the driver's fatigueness. This example uses the Haar Cascade algorithm, next to the OpenCV library to keep an eye on the real-time video of the driver and critique the driver's eyes. The Eye Aspect Ratio (EAR) is used in this measurement device to determine if the eyes are open or closed. The Mouth Aspect Ratio (MAR) is also used as an important element while this model describes the driver's fatigueness as the driver begins to yawn just before the driver feels fatigue. If the driver is found to be fatigue, a warning signal is issued. Keywords: OpenCV Automated algorithms, database, Image recognition, face detection, frontal postures, extraction phase, OpenCV, data models, HaarCascade classifier, training set.
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Vyshnavi, P. "Employee Attendance Management System using Face Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 5303–8. http://dx.doi.org/10.22214/ijraset.2021.36207.
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Automatic Facial Recognition Attendance System is a type of automated identification system that can recognize any person whose facial features have been saved in the database. This technology could be used by all corporations in the coming years, offices to keep track of who comes and goes. The attendance method is based on facial recognition technology. A real-time, contactless attendance tracking system that is extremely useful in today's world circumstances of a pandemic. After COVID, the work environment will not be the same. Despite the fact that the virus is still spreading, firms are attempting to restore on-premise operations in order to assure business continuity. Employees' health and safety are of utmost importance in such situations. Organizations are looking for methods to provide employees with a COVID-free workspace, and a touchless check-in is the first step. The attendance system uses a set of techniques like Haarcascade classifier and Local Binary Pattern Histogram(LBPH) Face Recognizer in deep learning to detect people in front of the camera and then changes their attendance in the attendance sheet automatically.
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G Kanishka, N. Hajira Banu, and B. Bhagya Lakshmi. "Smart Workforce Analytics: Optimized Adaptive Recognition with Feature Selection." International Research Journal of Innovations in Engineering and Technology 09, Special Issue (2025): 407–14. https://doi.org/10.47001/irjiet/2025.inspire66.
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In businesses, organizations, and educational institutions, preserving music of attendance is a everyday responsibility. Often, it`s miles completed thru manner of manner of hand using techniques like calling out roll numbers or names. The reason of this venture is to create a complex face recognition-based totally completely without a doubt honestly truely surely in truth attendance device that lets in you to replace and streamline the cutting-edge manual process. Developing an automated device on the way to growth the precision and effectiveness of record-preserving is our primary reason. Student information, collectively with name, roll number, class, section, and photographs, is professional and stored thru manner of manner of the technology, this is installation in classrooms. To extract pictures, OpenCV is utilized. The device can be approached thru manner of manner of university college university college university college university college students preceding to class, and it`ll snap their pictures and feature a have a take a have a study them to a pre-made dataset. To find out faces, the picturegraph processing technique first employs a Haarcascade classifier.
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Patel, Asst Prof Kajal, Ms Anamika Zagade, and Mr Deven Gupta. "Automated Facial Authentication Attendance System." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 509–18. http://dx.doi.org/10.22214/ijraset.2024.59809.
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Abstract: This research paper introduces a novel approach to automate attendance tracking in educational institutions through the implementation of a Face Recognition-based attendance system using Python. Traditionally, attendance management has relied on manual processes, prone to errors and time-consuming activities such as roll-call or name calling. The primary objective of this project is to revolutionize attendance management by developing an automated system that utilizes facial recognition technology. By leveraging modern advancements in computer vision, this system aims to streamline the attendancetaking process, enhancing efficiency and accuracy while reducing administrative burdens.Implemented within the classroom environment, the system captures student information including name, roll number,admission number, class, department, and photographs for training purposes. Utilizing OpenCV for image extraction and processing.The workflow involves initial face detection using a Haarcascade classifier, followed by facial recognition utilizing the LBPH (Local Binary Pattern Histogram) Algorithm. Upon recognition, the system cross-references the captured data with an established dataset to automatically mark attendance. Furthermore, to facilitate easy record-keeping, an Excel sheet is dynamically generated and updated at regular intervals with attendance information, ensuring seamless integration with existing administrative processes. This research provides a practical solution for attendance management and also helps in broader discourse on leveraging emerging technologies for optimizing educational and organizational workflows
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koli, Anuradha. "Face Recognition Attendance System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34827.
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Attendance taking is one of a tedious and important tasks in colleges, universities, organizations, schools, and offices, which must be done on a daily basis. The majority of the time, it is done manually, such as by calling by name or by roll number. The main goal of this project is to create a Face Recognition-based attendance system that will turn this manual process into an automated one. This project meets the requirements for bringing modernization to the way attendance is handled, as well as the criteria for time management. This device is installed in the classroom, where and student's information, such as name, roll number, class, sec, and photographs, is trained. The images are extracted using Open CV. Before the start of the corresponding class, the student can approach the machine, which will begin taking pictures and comparing them to the qualified dataset. Logitech C270 web camera and NVIDIA Jetson Nano Developer kit were used in this project as the camera and processing board. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor.
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Archana, Balkrishna Yadav. "Towards Real-Time Facial Emotion-Based Stress Detection Using CNN and Haar Cascade in AI Systems." International Journal of Engineering and Management Research 14, no. 5 (2024): 83–88. https://doi.org/10.5281/zenodo.14064731.
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Understanding human conduct requires the ability to recognise facial emotions, which has applications in everything from human-computer interaction to psychological wellness monitoring. This research provides a new approach to stress detection using Convolutional Neural Networks (or CNNs) and HaarCascade classifiers. The suggested method uses a CNN to recognise facial expressions and Haar Cascade algorithm for face detection. The methodology begins with preliminary processing the input photos, followed by face detection and extraction of facial regions. Those parts are then fed into the CNN model, which classifies emotions. The system has been trained and tested on publicly available datasets, with encouraging results in stress detection accuracy. This method, which detects stress through facial expressions, has potential uses in stress management, mental health evaluation, and personalised therapies. Face expressions have an important part in transmitting emotions, especially stress, which is a common problem in today's fast-paced world. This research provides a novel approach for detecting stress by analysing facial expressions with Convolutional Neural Networks(CNNs)and Haar Cascade classifiers. The proposed system enhances the precision and effectiveness of stress detection by combining the benefits of both approaches. The methodology begins by preprocessing the input photos to improve their quality and normalise them for subsequent analysis. Haar Cascade classifiers are then used to detect faces in the images, ensuring precise identification of facial regions even under different lighting conditions and orientations. The discovered faces are removed and resized to produce homogeneous inputs for further processing.
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-, Marrapu Anusha, Bevara Ramu -, Kalidindi Swathi -, Golajapu Venu Madhava Rao -, Manda Sravanthi -, and Andavarapu Mounika -. "Virtual Trailroom using Machine Learning." International Journal For Multidisciplinary Research 6, no. 3 (2024). http://dx.doi.org/10.36948/ijfmr.2024.v06i03.19971.
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The virtual trail room system receives a real-time video feed from a camera and processes data with the OpenCV computer vision library and Haarcascade classifier. In order to correctly identify faces in the video feed, the Haarcascade classifier is trained on a sizable dataset of human faces.The virtual room system may function independently, which eliminates the need for additional employees and associated expenditures, in contrast to typical security systems that demand the presence of physical security officers.
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Jai, Kuber Abrol, and Stuti Saxena. "INTELLIGENT FACE TRACKING ATTENDANCE SYSTEM USING LBPH AND KALMAN FILTERING." International Journal of Research -GRANTHAALAYAH 12, no. 1 (2024). https://doi.org/10.29121/granthaalayah.v12.i1.2024.6107.
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Attendance tracking remains a crucial daily task in educational institutions and corporate environments. Traditionally performed manually, this process is often time-consuming and error-prone. To modernize and streamline attendance management, this project proposes a Facial Recognition-based Automated Attendance System that utilizes real-time image processing enhanced with a Kalman Filter for robust face tracking. The system captures and identifies student faces using a Logitech C270 webcam connected to an NVIDIA Jetson Nano Developer Kit. Initial face detection is performed using the Haarcascade classifier, followed by facial recognition through the LBPH (Local Binary Pattern Histogram) algorithm. The integration of the Kalman Filter enables smooth and continuous tracking of facial features, compensating for occlusions, motion blur, and abrupt student movements, thereby improving recognition accuracy and system responsiveness. The system maintains a dynamic attendance log by automatically cross-referencing recognized faces with a pre-trained dataset containing student details such as name, roll number, class, and section. Attendance records are updated hourly and stored in an Excel sheet accessible by the instructor. This approach ensures a contactless, efficient, and reliable solution for attendance monitoring in real-world classroom environments.
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Rohini, Valaparla, M. Sobhana, and Ch Smitha Chowdary. "Attendance Monitoring System Design Based on Face Segmentation and Recognition." Recent Patents on Engineering 16 (April 1, 2022). http://dx.doi.org/10.2174/1872212116666220401154639.
Full textAbstract:
Aim: The proposed work aims to monitor real-time attendance using face recognition in every institutional sector. It is one of the key concerns in every organization. Background: Nowadays, most organizations spend a lot of time marking attendance for a large number of individuals manually. Many technologies like Radio Frequency Identification (RFID), and biometric systems are introduced to overcome the manual attendance system. However, not all of these technologies are automatic, and people must queue to have their presence recorded. Objective: The main objective of the system is to provide an automated attendance system with the help of face recognition owing to the difficulty in the manual as well as other traditional attendance systems. Methods: The main objective of the system is to provide an automated attendance system with the help of face recognition owing to the difficulty in the manual as well as other traditional attendance systems. Results: Using the web camera connected to the computer, face detection and recognition are performed, and recognized faces are attended. Here, the admin module and teacher modules are implemented with different functionalities to monitor attendance. Conclusion: Experiment results get 94.5% accuracy of face detection and 98.5% accuracy of face recognition by using the Haarcascade classifier and LBPH algorithm. This application system will be simple to implement, accurate, and efficient in monitoring attendance in real-time.
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-, Anuj Singh, Nikhil Rawat -, and Rajan Kesri -. "Face Recognition Based Attendance System." International Journal For Multidisciplinary Research 5, no. 3 (2023). http://dx.doi.org/10.36948/ijfmr.2023.v05i03.3320.
Full textAbstract:
In colleges, universities, organizations, schools, and offices, taking attendance is an important tasks that must be done on a daily basis. The majority of the time, it is done manually, such as by calling by name or by roll number. The goal of this project is to create a Face Recognition-based attendance system that will turn this manual process into an automated one making our task easy. This project meets the requirements for bringing modernization to the way attendance is handled, as well as the criteria for time management. This device is installed in the classroom, where and student's information, such as name, roll number, class, section, and photographs, is trained and is also available for teachers and staff attendance. The images are extracted using Open CV a library of python. Before the start of the corresponding class, the student can approach the machine, which will begin taking pictures and comparing them to the qualified dataset. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor.
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Manas Dabhane, Vaishnavi Chamatkar, Vedant Dhatrak, and Dr. Bireshwar Ganguly. "Smart Face Attendance System." International Journal of Advanced Research in Science, Communication and Technology, December 3, 2023, 49–59. http://dx.doi.org/10.48175/ijarsct-14006.
Full textAbstract:
In colleges, universities, organizations, schools, and offices, taking attendance is one of the most important tasks that must be done on a daily basis. The majority of the time, it is done manually, such as by calling by name or by roll number. The main goal of this project is to create a Face Recognition-based attendance system that will turn this manual process into an automated one. This project meets the requirements for bringing modernization to the way attendance is handled, as well as the criteria for time management. This device is installed in the classroom, where and student's information, such as name, roll number, class, sec, and photographs, is trained. The images are extracted using Open CV. Before the start of the corresponding class, the student can approach the machine, which will begin taking pictures and comparing them to the qualified dataset. Logitech C270 web camera and NVIDIA Jetson Nano Developer kit were used in this project as the camera and processing board. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor
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Dr. Umesh B. Pawar, Rupali D. Kudal, Ashwini M. Lokhande, Tejashree V. Gaikwad, and Gayatri R. Bhagwat. "Face Recognition Based Attendance Software and Reporting using OpenCV." International Journal of Advanced Research in Science, Communication and Technology, December 19, 2023, 616–21. http://dx.doi.org/10.48175/ijarsct-14282.
Full textAbstract:
The face is the identity of a person. The methods to exploit this physical feature have seen a great change since the advent of image processing techniques. The attendance is taken in every school, colleges and libraries. Traditional approach for attendance is professor calls student name and record attendance. It takes some time to record attendance. For each lecture this is a waste of time. To avoid these losses, we're going to use automatic process which is based on image processing. Identification of any people in any organization or colleges for the purpose of attendance marking is one of such software. Authentication is a significant issue in computer-based communication. Human face recognition is an important branch of biometric verification and has been widely used in many applications. The use of Attendance Management System is to perform the regular activities of attendance marking and analysis with less human intervention. In this new approach, we are using face detection and face recognition system. This face detection distinguishes faces from non-faces and is essential for accurate attendance. The other strategy is facing recognition for marking the student’s attendance. We have used OpenCV for this system. The camera will be connected using OpenCV module that converts the image into the RGB format which in turn is mapped to the pre-trained neural networks using HOG algorithm to recognize the face pixels. This system offers effective way to manage the attendance system which is time efficient and offers massive scaling for future purposes. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor.
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Ashish, Singh, Pankaj, Tanisha Km., Tanu Km., and Swati Km. "Ai Based Face Recognition Attendance System &RFID Door Lock." August 26, 2023. https://doi.org/10.5281/zenodo.8285965.
Full textAbstract:
The traditional method of taking attendance has become outdated in this age of rapidly evolving new technologies. The AI-based attendance system is a technological solution that automates the process of taking attendance with high accuracy and efficiency using advanced technologies such as facial recognition, biometrics, and machine learning algorithms. The system combines various technologies to provide accurate attendance tracking and access control. It includes a door lock that only opens for authorized individuals and an attendance tracking system that uses facial recognition, RFID, and machine learning algorithms to identify and record attendance data of individuals. The facial recognition technology captures images of individuals and trains them using machine learning algorithms, ensuring that only authorized individuals can access the system and havetheir attendance recorded. The system also includes RFID technology, which provides access to the premises, and a web portal that allows authorized personnel to check attendance in real- time. The system is highly scalable, customizable, easy to use, and requires minimal training, making it suitable for small and large organizations. In summary, the AI-based attendance system is an innovative solution that provides accurate attendance tracking and access control, ensuring that only authorized individuals have access to the facility. The system is scalable, customizable, easy to use, and requires minimal training, making it a flexible solution for schools, universities, and organizations that require strict access control and efficient attendance tracking.
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N., N. Mosola, J. Molete S., S. Masoebe L., and Letsae M. "Hand Gesture Detection via EmguCV Canny Pruning." International Journal of Information, Control and Computer Sciences 11.0, no. 6 (2018). https://doi.org/10.5281/zenodo.1316863.
Full textAbstract:
Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.