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Journal articles on the topic 'Haar cascade model'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Hall, Wayne, Andreas Kromik, Brenton Miller, Ian Underhill, and Zia Javanbakht. "A Machine Learning Model for Flaw Identification in Fibre-Reinforced Composites." Materials Science Forum 1094 (July 27, 2023): 5–10. http://dx.doi.org/10.4028/p-igdb3j.

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A Haar cascade classifier is a machine learning (ML) algorithm used for object detection. In this paper, the Haar algorithm is introduced in the context of a non-destructive evaluation of fibrereinforced composite (FRC) structures. The Haar learning model is used for flaw identification from thermal images. Thermal images are created from cross-ply (CP) carbon fibre-reinforced laminates with flat-bottomed holes (6–10 mm) of different depths from the surface (0.5–1.5 mm). After training is complete, the model successfully detects similar artificial flaws in previously unseen thermal images. In

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Heryana, Nono, Rini Mayasari, and Kiki Ahmad Baihaqi. "Penerapan Haar Cascade Classification Model Untuk Deteksi Wajah, Hidung, Mulut, dan Mata Menggunakan Algoritma Viola-Jones." Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi 5, no. 1 (2020): 21–25. http://dx.doi.org/10.36805/technoxplore.v5i1.1064.

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Penelitian ini dilakukan untuk melakukan deteksi fitur yang ada pada wajah manusia, pendekatan yang digunakn dalam penelitian ini adalah Penerapan Haar Cascade Classification Model Untuk Deteksi Wajah, Hidung, Mulut, Mata Menggunakan Algoritma Viola-Jones sehingga sistem yang dihasilkan mampu untuk melakukan deteksi terhadap fitur-fitur yang ada pada wajah manusia yang meliputi Wajah, Hidung, Mulut, dan Mata. Dalam penerapan deteksi wajah, hidung, mulut dan mata ini dibangun menggunakan metode viola-jones yang terdiri dari metode haar-like feature, citra integral, adaboost, dan cascade of clas

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Irawanto, Indra, Andi Sunyoto, and Kusnawi Kusnawi. "Peningkatan Akurasi Deteksi Kendaraan Menggunakan Kombinasi Haar Cascade Classifier dan Convolutional Neural Networks (CNN)." Journal of Electrical Engineering and Computer (JEECOM) 6, no. 1 (2024): 47–57. http://dx.doi.org/10.33650/jeecom.v6i1.8242.

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Teknologi pengolahan citra digital dan computer vision telah memainkan peran penting dalam meningkatkan sistem pengaturan lalu lintas. Meskipun kamera CCTV umum digunakan, kebanyakan sistem masih bersifat pasif dan terbatas dalam pengawasan arus lalu lintas. Dalam menanggapi kebutuhan akan sistem yang lebih proaktif dan adaptif, dikembangkan berbagai sistem Manajemen Lalu Lintas Pintar yang mengintegrasikan teknologi deteksi objek kendaraan canggih, seperti kombinasi Haar Cascade Classifier dengan Convolutional Neural Network (CNN). Haar Cascade Classifier efektif dalam mendeteksi objek real-t

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Andrean, Muhammad Niko, Guruh Fajar Shidik, Muhammad Naufal, et al. "Comparing Haar Cascade and YOLOFACE for Region of Interest Classification in Drowsiness Detection." JURNAL MEDIA INFORMATIKA BUDIDARMA 8, no. 1 (2024): 272. http://dx.doi.org/10.30865/mib.v8i1.7167.

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Driver drowsiness poses a serious threat to road safety, potentially leading to fatal accidents. Current research often relies on facial features, specific eye components, and the mouth for drowsiness classification. This causes a potential bias in the classification results. Therefore, this study shifts its focus to both eyes to mitigate potential biases in drowsiness classification.This research aims to compare the accuracy of drowsiness detection in drivers using two different image segmentation methods, namely Haar Cascade and YOLO-face, followed by classification using a decision tree alg

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Saragih, Silvanus. "Implementasi Algoritma Haar Cascade Menggunakan Pengolahan Citra Digital untuk Absensi Deteksi Wajah dan Nama Menggunakan Python." Jurnal Sosial Teknologi 5, no. 3 (2025): 789–98. https://doi.org/10.59188/jurnalsostech.v5i3.32044.

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Sistem Penelitian ini bertujuan untuk mengimplementasikan algoritma Haar Cascade dalam pengembangan sistem absensi berbasis deteksi wajah dan nama dengan menggunakan bahasa pemrograman Python. Haar Cascade merupakan metode populer untuk deteksi objek, khususnya wajah, yang memanfaatkan pemrosesan citra dan fitur Haar. Metode ini bekerja dengan melatih model menggunakan dataset wajah, kemudian menerapkannya untuk mendeteksi wajah dan menghubungkannya dengan data nama individu yang telah terdaftar. Penelitian ini mencakup pengumpulan dataset wajah, pelatihan model menggunakan OpenCV dalam Python

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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.

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Kumar, Meesala Sai. "Image Similarity Using Logistic Regression." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40562.

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Many machine learning algorithms, such as kernel machines, nearest neighbors, clustering, and anomaly detection, rely on distances or similarities to identify patterns in data. Before using these similarities to train a model, it is crucial to ensure they reflect meaningful relationships within the data. In this paper, we propose enhancing the interpretability of these similarities by augmenting them with explanations. To achieve this, we introduce Logistic Regression & Haar Cascade, a scalable and theoretically sound method designed to systematically decompose the output of pre-trained de

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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 mo

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Joodi, Mohanad Azeez, Muna Hadi Saleh, and Dheya Jassim Khadhim. "Proposed Face Detection Classification Model Based on Amazon Web Services Cloud (AWS)." Journal of Engineering 29, no. 4 (2023): 176–206. http://dx.doi.org/10.31026/j.eng.2023.04.12.

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One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resourc

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Junaid Malik, Mohammad, Mathamsetti Aditya, D. Rohith Surya Teja Varma, et al. "Specific Object Picking Robotic Arm Using Haar Cascades." E3S Web of Conferences 529 (2024): 04008. http://dx.doi.org/10.1051/e3sconf/202452904008.

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This project shows a robotic arm that can pick up objects. It was made with accuracy and speed in mind for use in factories. The robotic arm is very good at picking up metal nuts. It uses cutting edge technologies, like Haar cascade to find objects and inverse kinematics to figure out angles very accurately, to make its moves more exact and dexterous. A powerful computer vision method called Haar cascade is used to find metal nuts in the robotic arm's working environment. To do this, positive and negative pictures are used to train a Haar cascade classifier, which makes a model that can recogn

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Arju, Bano, Akash Saxena Dr., and Kumar Das Gaurav. "AN EFFICIENT DETECTION APPROACH OF DRIVER- DROWSINESS USING MULTIPLE CONVOLUTIONAL HAAR CASCADE KERNELIZED CNN (MCHCKCNN) ALGORITH." AN EFFICIENT DETECTION APPROACH OF DRIVER- DROWSINESS USING MULTIPLE CONVOLUTIONAL HAAR CASCADE KERNELIZED CNN (MCHCKCNN 02, no. 02 (2021): 165–71. https://doi.org/10.5281/zenodo.5060282.

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A lot of detail is transmitted by the face, an essential part of the body. If there is a car in a facial movement, for example, the frequency of yawning and blinking is distinct from that of fatigue state. It’s in its natural state. We suggest a new system to determine the standard of the driver. Centered on face monitoring and facial main point identification of fatigue. We are developing a new algorithm and proposing the Kernelized Convolutional Neutral Network Multiple Convolutional Haar Cascade (MCHC-KCNN) Algorithm for monitoring the face of the driver using CNN and MCHC and give 0.

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Lijaya Therry, Renaldy William, Zacky Yaser Malik Gumiwang, and Wahyu S. J. Saputra. "Pendeteksi Masker Pada Wajah Dengan Menggunakan Algoritma Haar Cascade Classifier." Jurnal Manajamen Informatika Jayakarta 2, no. 3 (2022): 224. http://dx.doi.org/10.52362/jmijayakarta.v2i3.831.

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Hanya dua hal yang dapat melawan pandemi saat ini adalah masker wajah dan pembersih. Tapi ada orang yang tidak mematuhi aturan atau peraturan apa pun. Untuk membatasi orang-orang dari datang ke tempat-tempat umum dan menjaga diri serta orang lain dalam bahaya, perlu ada sebagai mekanisme untuk cepat, efisien dan sederhana Metode Deteksi Masker Wajah di tempat umum. Karya ini berfokus pada pengembangan model Untuk Masker Wajah deteksi yang dapat dengan mudah dipasang di tempat-tempat umum seperti stasiun kereta api, halte bus, rumah sakit, belanja mal, kantor, stadion dll. Model yang diusulkan

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Adeshina, Sirajdin Olagoke, Haidi Ibrahim, Soo Siang Teoh, and Seng Chun Hoo. "Custom Face Classification Model for Classroom Using Haar-Like and LBP Features with Their Performance Comparisons." Electronics 10, no. 2 (2021): 102. http://dx.doi.org/10.3390/electronics10020102.

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Face detection by electronic systems has been leveraged by private and government establishments to enhance the effectiveness of a wide range of applications in our day to day activities, security, and businesses. Most face detection algorithms that can reduce the problems posed by constrained and unconstrained environmental conditions such as unbalanced illumination, weather condition, distance from the camera, and background variations, are highly computationally intensive. Therefore, they are primarily unemployable in real-time applications. This paper developed face detectors by utilizing

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Chau, Khanh Ngan, and Nghi Thanh Doan. "DENSE SIFT FEATURE AND LOCAL NAIVE BAYES NEAREST NEIGHBOR FOR FACE RECOGNITION." Scientific Journal of Tra Vinh University 1, no. 28 (2017): 56–63. http://dx.doi.org/10.35382/18594816.1.28.2017.46.

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Human face recognition is a technology which is widely used in life. There have been much effort on developing face recognition algorithms. In this paper, we present a new methodology that combines Haar Like Features - Cascade of Boosted Classifiers, Dense Scale-Invariant Feature Transform (DSIFT), Local Naive Bayes Nearest Neighbor (LNBNN) algorithm for the recognition of human face. We use Haar Like Features and the combination of AdaBoost algorithm and Cascade stratified model to detect and extract the face image, the DSIFT descriptors of the image are computed only for the aligned and crop

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Nazarkevych, Mariia, Vasyl Lytvyn, and Victoria Vysotska. "METHOD OF RECOGNITION OF MOVING OBJECTS BASED ON THE CLASSIFICATION OF HAAR CASCADES." Cybersecurity: Education, Science, Technique 2, no. 26 (2024): 361–73. https://doi.org/10.28925/2663-4023.2024.26.698.

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A method of recognition of moving objects in a video stream based on the Haar classification has been developed. When tracking objects, there is a need to identify them and record their direction of movement, speed of movement. The complexity of recognition lies not only in fixing the object and following it, but also in the movement of the camera itself, from which video surveillance is conducted. The Haar method is based on cascade classifiers that quickly highlight regions with a high probability of detecting an object. Haar cascades use a convolution operation, which is formed on the basis

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Ozioma, Collins Oguine, Jane Oguine Kanyifeechukwu, Ibrahim Bisallah Hashim, and Ofuani Daniel. "Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction." BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning 1, no. 1 (2022): 63–71. http://dx.doi.org/10.54646/bijiam.011.

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Facial expression recognition is a vital research topic in most fields ranging from artificial intelligence and gaming to human–computer interaction (HCI) and psychology. This paper proposes a hybrid model for facial expression recognition, which comprises a deep convolutional neural network (DCNN) and a Haar Cascade deep learning architecture. The objective is to classify real-time and digital facial images into one of the seven facial emotion categories considered. The DCNN employed in this research has more convolutional layers, ReLU activation functions, and multiple kernels to enhance fil

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Sadhana, Paladugu. "Deployment of a Dog Breed Classifier Using CNN and ResNet-50 on AWS EC2." International Journal of Leading Research Publication 5, no. 9 (2024): 1–3. https://doi.org/10.5281/zenodo.14880666.

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This paper presents the development and deployment of a machine learning model for dog breed classification. The model leverages Haar Cascade classifiers for initial detection and Convolutional Neural Networks (CNN) with ResNet-50 for classification. The deployment was carried out on AWS EC2, encountering challenges related to maintaining a live production link with continuous updates. The study discusses model performance, deployment considerations, and solutions to challenges encountered in production.

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Pradana, Afu Ichsan. "Deteksi Ketepatan Pengunaan Masker Wajah dengan Algoritma CNN dan Haar Cascade." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 9, no. 3 (2022): 2305–16. http://dx.doi.org/10.35957/jatisi.v9i3.2912.

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Penggunaan masker wajah di ruang publik merupakan upaya pemerintah dalam menekan penyebaran COVID-19. Saat ini pengawasan penggunaan masker wajah masih dilakukan dengan cara manual yaitu petugas yang melakukan pemantauan langsung dilapangan, sehingga pemantauan tidak dapat dilakukan sepanjang waktu. Dalam pemantauan penggunaan masker wajah telah banyak pengembangan terutama di bidang visi komputer dengan menggunakan berbagai macam metode deteksi yaitu YOLO, Convolutional Neural Network (CNN), Viola-Jones atau Haar Cascade, dan Hybrid Deep Transfer Learning.  Dari beberapa penerapan metod

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Rahmatunnisa, Elsa Nabila, Billy Adrian Fernanda, Yusuf Maulana, and Ali Maulana Hapid Aripin. "Implementasi Sistem Keamanan Rumah Berbasis Pengenalan Wajah untuk Peningkatan Keamanan Residensial." Infotek : Jurnal Informatika dan Teknologi 7, no. 1 (2024): 205–15. http://dx.doi.org/10.29408/jit.v7i1.23868.

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The objective of this research is to develop a home security system that utilizes facial recognition technology using the Haar Cascade method and ESP32CAM. In the face of criminal threats, the importance of an effective home security system is increasing. The facial recognition method employed in this study is the Haar Cascade method, which utilizes specific facial features to identify individual identities. The ESP32CAM sensor is used as a camera to capture facial images of the home occupants. The research involves the process of collecting facial image data from the home occupants, which is

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Patel, Chintan. "Mask detection as Covid combat using Transfer learning and Haar Cascade." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 502–9. http://dx.doi.org/10.22214/ijraset.2021.38837.

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Abstract: The World is going through a pandemic due to the rapid transmission of COVID-19. According to the several guidelines issued by WHO (World Health Organization), wearing a mask is the most effective preventive measure in public/crowded places. We hope for the future social health and safety of the people around the world with this project. To detect the people who are not following the COVID-19 guidelines in public/crowded areas a convolutional neural network under the framework of the TensorFlow VGG-19 algorithm is proposed which has trained and tested a collection of more than 1350 i

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Elbert, Endah Setyaningsih, and Lamto Widodo. "Comparative Analysis of Haar Cascade Classifier, Dlib, and Mediapipe for Face Recognition." ELECTRON Jurnal Ilmiah Teknik Elektro 6, no. 1 (2025): 1–8. https://doi.org/10.33019/electron.v6i1.240.

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Technological developments are having considerable effects on a lot of industries, particularly in the security sector. One of the important technologies in security sector is face recognition. Face recognition is a technology that verify and identify individual identity using face. There are many processes that involved in face recognition technology such as face detection methods. Face detection is a process of searching for faces in images. Each face detection method has different way to searching the face in image. It can affect the performance of face recognition technology itself. In thi

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Thapliyal, Amitabh, Om Prakash Verma, and Amioy Kumar. "Mask Covered Face Recognition Using Haar Cascade Classifier and Fuzzy Logic." International Journal of Emerging Technology and Advanced Engineering 12, no. 8 (2022): 152–66. http://dx.doi.org/10.46338/ijetae0822_19.

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Right from the beginning of the COVID-19 outbreak, everyone is aware of the havoc caused by the pandemic. To curb its spread, every healthcare agency and civic body around the globe has been advising to wear masks. However, this necessary practice has posed a significant challenge for the modern-day Facial Recognition technology. Face recognition finds significant application in the security domain that demands speed and accuracy both simultaneously. This requires the system to be highly optimized and efficient. Through this paper, we present a novel approach using Haar cascade classifier for

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Sitompul, Joshua, M. Irwan Bustami, and Desi Kisbianty. "Implementasi Algoritma Haar Cascade Classifier Dalam Mendeteksi Robot Sepak Bola Beroda." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 4 (2022): 2032. http://dx.doi.org/10.30865/mib.v6i4.3929.

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In soccer robot contests, generally soccer robots can recognize their own team robots through color detection using the HSV model. Robots that use color detection to identify their own team robot can detect objects that have the same color value, so objects that have the same color will be considered as their own team robot. However, if you only rely on this method, it is still lacking when viewed in terms of object tracking. Haar like feature or known as Haar Cascade Classifier is a rectangular (square) feature method, which gives a specific indication of an image. This method is able to dete

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Kumar, Sanjeev, Mohit Kumar, Kriti Dubey, and Kaushal Sharma. "Unveiling unmasked faces: A novel model for improved mask detection using haar cascade technique." Journal of Soft Computing Exploration 4, no. 3 (2023): 115–22. http://dx.doi.org/10.52465/joscex.v4i3.179.

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In response to the urgent need to enforce mask-wearing compliance during the COVID-19 pandemic, this "Face Mask Detection" project introduces a robust model for identifying individuals not wearing face masks in videos. Leveraging computer vision's Haar Cascade technique, the project achieves rapid face detection within video streams, facilitating accurate mask usage assessment. This initiative holds paramount importance due to the pivotal role of masks in curbing virus spread. The model finds practical applications in monitoring mask adherence in public settings, pinpointing potential COVID-19

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Karrothu, Aravind, T. Lavanya, V. K. Harish, and N. K. Ramesh. "A heuristic technique for music recommendation using Haar cascade classifier through facial expressions." Applied and Computational Engineering 4, no. 1 (2023): 255–59. http://dx.doi.org/10.54254/2755-2721/4/20230461.

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Human emotion assumes a fundamental part lately. Feeling depends on human sentiments which can be both communicated or not. Feelings communicate the human's singular way of behaving which can be in various structures. Extraction of the emotion states people individual condition of conduct. The goal of this task is to separate element from human face and identify feeling and to play music as indicated by the emotion identified. In this task, fostering a model to suggest dynamic music proposal framework in view of human feelings is our principal perspective. From the genuine face the emotion is

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Hustinawaty and Muhammad Farell. "Implementation of Mask Use Detection With SVM and Haar Cascade in OpenCV." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 13, no. 1 (2024): 31–37. http://dx.doi.org/10.22146/jnteti.v13i1.9292.

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Despite a decline in global COVID-19 cases, the persisting threat of SARS-CoV-2 coupled with waning public awareness of the virus threat has raised concerns. A notable number of individuals disregard mask usage or do so incorrectly. It is particularly concerning given that COVID-19 has high transmissibility, especially in crowded areas like shopping centers. Enforcement officers often face challenges in identifying those wearing masks improperly. Herein lies the significance of automated mask detection to aid enforcement officers in containing the spread of the virus. Hence, this paper aims to

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Pillai, Bharath S., and Ganesh Natesan. "Face Recognition Attendance System using Haar Cascade Classifier and Local Binary Pattern Histogram Algorithm." International Journal of Innovative Research in Computer and Communication Engineering 12, no. 03 (2024): 1670–73. http://dx.doi.org/10.15680/ijircce.2024.1203049.

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Traditional classroom attendance methods, like roll-call and sign-in sheets, are time-consuming and error-prone. This paper introduces a cost-effective solution leveraging Real-Time Face Recognition to efficiently manage student attendance. The proposed model, implemented in Python with OpenCV, uses Haar Cascade for face detection and LBPH for recognition, considering both positive and negative facial features for accuracy. The Tkinter GUI interface enhances user interaction, marking a departure from cumbersome traditional approaches and addressing the challenges of managing large student grou

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Arifin, Syamsul, Aulia Siti Aisjaha, Azzezza Nurul Fatima, and Haniah Mahmudah. "Design and Development of a System for Monitoring Student Attention and Concentration during Learning using CNN Model and Face Landmark Detection." JOIV : International Journal on Informatics Visualization 9, no. 1 (2025): 201. https://doi.org/10.62527/joiv.9.1.2897.

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Mobile learning media has been wide and provides a tendency for lecturers to identify students' concentration levels in online classes. To bring the class into active learning, efforts are needed from lecturers and educational institutions to return students' concentration to the ongoing learning process. In this paper, a monitoring and alarm system is designed to increase student concentration and combines two elements of statistical analysis to validate CNN models that recognize face emotions in real time while learning. The research was carried out by recording face data using a camera, ext

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Kousar, Abdul Majeed, Abbas Zain, Bakhtyar Maheen, et al. "Face Detectors Evaluation to Select the Fastest among DLIB, HAAR Cascade, and MTCNN." Pakistan Journal of Emerging Science and Technologies 2, no. 1 (2021): 13. https://doi.org/10.5281/zenodo.5089391.

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Face detection is an important problem in computer vision research and applications are getting trending due to the advancement in the file of machine learning and computer vision. This research proposed a face detection method based on an enhanced Multi-Task Convolution Neural Network (MTCNN) and improves the network of MTCNN, creates a neural network model based on MTCNN using Python, and cascades to increase the accuracy of face location in difficult scenarios. In this research paper, we evaluated the performance of three famous face detector models on CPU-based machines.  MTCNN a

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Zhao, Yu Dan, Jing Wen Xu, Jun Fang Zhao, Xin Li, and Shuang Liu. "Study on Insect Pests Detection Based on Digital Image." Applied Mechanics and Materials 701-702 (December 2014): 357–60. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.357.

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This paper mainly performs Cascade AdaBoost algorithm based on multi-feature to detect the images of Eurydema dominulus, which will cause harm to crucifer. Firstly, the mixing of HAAR features and LBP features is adopted instead of the single-feature of traditional model, which makes description of images more comprehensively from the angle of the gradient and texture. And then use the best features selected by Gentle AdaBoost algorithm to compose the weak classifier and the strong classifier. And the cascade detector is composed of the trained classifiers of each layer according to a certain

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Ramos, Anna Liza A., Bless L. Reyes, Jomar J. Nuevo, Paulo A. Avila, Eugene A. Bas, and Patrick June M. Gonzales. "Facial Recognition Performance Based on the Lighting Set-Up Models Applied to Home Security Door Access using Principal Component Analysis and Raspberry Pi Controller." Innovatus 2, no. 1 (2019): 40–46. https://doi.org/10.5281/zenodo.5195596.

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Security protects individuals, data, and properties together with its corresponding measures in line to the emerging application of technology like the face recognition. This study aims to test the effects of lighting models on the recognition performance along with different angles and distance applied for door access by providing signals to Raspberry PI Controller. The study built 240 training datasets and applied the best algorithms – Haar-Cascade for face detection, Principal Component Analysis for extraction, Support Vector Machine for classification and Euclidean Distance for recog

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Maryuni Susanto, Bekti, Surateno Surateno, Ery Setiyawan Jullev Atmadji, et al. "Face recognition using haar cascade classifier and FaceNet (A case study: Student attendance system)." International Journal of Informatics and Communication Technology (IJ-ICT) 13, no. 2 (2024): 272. http://dx.doi.org/10.11591/ijict.v13i2.pp272-284.

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Face recognition is increasingly widely utilised, and there are numerous face recognition systems. Face recognition is typically utilised for attendance on e-learning platforms in the field of education. The haar cascade classifier is one method for face identification; it is used to identify facial areas. Faces are classified using an alternative model, FaceNet. In this research, we purposefully designed an e-learning platform that authenticates students based on face recognition. Based on the findings of this investigation, the system can accurately recognise faces. Ten students were evaluat

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Bekti, Maryuni Susanto, Surateno, Setiyawan Jullev Atmadji Ery, et al. "Face recognition using haar cascade classifier and FaceNet (A case study: Student attendance system)." International Journal of Informatics and Communication Technology 13, no. 2 (2024): 272–84. https://doi.org/10.11591/ijict.v13i2.pp272-284.

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Face recognition is increasingly widely utilised, and there are numerous face recognition systems. Face recognition is typically utilised for attendance on e-learning platforms in the field of education. The haar cascade classifier is one method for face identification; it is used to identify facial areas. Faces are classified using an alternative model, FaceNet. In this research, we purposefully designed an e-learning platform that authenticates students based on face recognition. Based on the findings of this investigation, the system can accurately recognise faces. Ten students were evaluat

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Yeh, Jui-Feng, Kuei-Mei Lin, Chia-Chen Chang, and Ting-Hao Wang. "Expression Recognition of Multiple Faces Using a Convolution Neural Network Combining the Haar Cascade Classifier." Applied Sciences 13, no. 23 (2023): 12737. http://dx.doi.org/10.3390/app132312737.

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Facial expression serves as the primary means for humans to convey emotions and communicate social signals. In recent years, facial expression recognition has become a viable application within medical systems because of the rapid development of artificial intelligence and computer vision. However, traditional facial expression recognition faces several challenges. The approach is designed to investigate the processing of facial expressions in real-time systems involving multiple individuals. These factors impact the accuracy and robustness of the model. In this paper, we adopted the Haar casc

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Indira, D. N. V. S. L. S., Nagamani Tenali, J. N. V. R. Swarup Kumar, et al. "ETMS: Efficient Traffic Management System for Congestion Detection and Alert using HAAR Cascade." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6 (2023): 159–66. http://dx.doi.org/10.17762/ijritcc.v11i6.7321.

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Rapid social development has resulted in the emergence of a new major societal issue: urban traffic congestion, which many cities must address. In addition to making it more difficult for people to get around town, traffic jams are a major source of the city's pollution crisis. In order to address the problems of automobile exhaust pollution and congestion, this paper uses the system dynamics approach to develop a model to study the urban traffic congestion system from the perspectives of trucks,private cars, bikes and public transportation. This project proposes a system for detecting vehicle

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Navlakhe, Vishakha V., and Deepak Kapgate. "Face Detection Using Skin Likelihood and Haar Features for Digital Video Processing." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, no. 1 (2015): 11–18. http://dx.doi.org/10.53555/nncse.v2i1.510.

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Face detection is an important early step in many computer vision systems. By using pixel-wise detectors, spatial analysis of skin probability and skin regions segmentation, a new method for face detection is introduced. In this project, we proposed and implemented a modified self-organizing mixture network (SOMN) which specifies the distribution of objects in image and skin and non skin color model, skin likely-hood to exactly identify skin region of interest from image. Bayesian Decision Rule is applied to specify c as skin color or non skin color. Finally, we are using haar like features to

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Umar Aditiawarman, Dimas Erlangga, Teddy Mantoro, and Lutfil Khakim. "Face Recognition of Indonesia’s Top Government Officials Using Deep Convolutional Neural Network." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 1 (2023): 113–19. http://dx.doi.org/10.29207/resti.v7i1.4437.

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Facial recognition is a part of Computer Vision that is used to get facial coordinates from an image. Many algorithms have been developed to support Facial Detection such as Cascade Face Detection using Haar-Like features and AdaBoost to classify its Cascade and Convolutional Neural Network (CNN). Face recognition in this study uses the Deep Convolutional Neural Network (DCNN) method, and the output of this method is the measurement value of the face. In the model training process, Triplet Loss from Triplet Network Deep Metric Learning is used to get good face grouping results. The value of th

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Joodi, Mohanad Azeez, Muna Hadi Saleh, and Dheyaa Jasim Kadhim. "Increasing validation accuracy of a face mask detection by new deep learning model-based classification." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (2023): 304–14. https://doi.org/10.11591/ijeecs.v29.i1.pp304-314.

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During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is bui

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Verdiansyah, Muhammad, and Achmad Solichin. "Penerapan Algoritma Convolutional Neural Network dan Haar Cascade Untuk Presensi Dengan Video Rekaman Zoom." Jurnal Ilmiah Informatika 7, no. 2 (2023): 116–27. http://dx.doi.org/10.35316/jimi.v7i2.116-127.

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The COVID-19 pandemic that is engulfing the world is one of the situations that urges the use of and even human dependence on technology to become higher. This pandemic that can cause death by spreading through droplets or water droplets, so many countries have implemented social restrictions by prohibiting their citizens from doing many activities outside. The use of technology such as the Google Meet and Zoom applications has become a new habit for the community, especially in learning and teaching. Attendance is important to know and control the presence of students in the teaching and lear

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AlKindy, Bassam, Oras B. Jamil, Huda Al-Nayyef, and Wissam Alkendi. "A Machine Learning Approach for Identifying Five Types of Horizontal Ocular Disorders Using Haar Features." Al-Mustansiriyah Journal of Science 36, no. 1 (2025): 69–83. https://doi.org/10.23851/mjs.v36i1.1597.

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Background: The proliferation of digital devices, such as smartphones, TVs, tablets, and laptops, has raised concerns about the potential long-term impact on eye health, particularly from blue light emitted by screens. Related studies suggested that prolonged exposure to blue light may contribute to visual impairments or discomfort. Objective: This research introduces an innovative machine learning approach aimed at diagnosing such visual impairments by automatically detecting the iris center in images using a combination of the Haar Cascade Classifier and Circular Hough Transform algorithm. M

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Vagmare, Rishikesh. "Emotion Recognition with CNN." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 2738–43. http://dx.doi.org/10.22214/ijraset.2023.57189.

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Abstract: Emotion is a subjective phenomenon, utilizing knowledge and science behind tagged data and extracting the components that comprise it has been a difficult challenge. With the advancement of deep learning in computer vision, emotion identification has become a popular research topic. This Project presents feature extraction of facial expressions using a neural network combination for the recognition of various facial emotions (sad, happy, neutral, angry, surprised, fear). Convolution Neural Network has been used to achieve a accuracy of 75%, which have excellent recognition of image f

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Ilmadina, Hepatika Zidny, Muhammad Naufal, and Dega Surono Wibowo. "Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 22, no. 3 (2023): 419–30. http://dx.doi.org/10.30812/matrik.v22i3.2785.

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Traffic accidents are fatal events that need special attention. According to research by the National Transportation Safety Committee, 80% of traffic accidents are caused by human error, one of which is tired and drowsy drivers. The brain can interpret the vital fatigue of a drowsy driver sign as yawning. Therefore, yawning detection for preventing drowsy drivers’ imprudent can be developed using computer vision. This method is easy to implement and does not affect the driver when handling a vehicle. The research aimed to detect drowsy drivers based on facial expression changes of yawning by c

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Hu, Jianjun, Yuqi Sun, and Songsong Xiong. "Research on the Cascade Vehicle Detection Method Based on CNN." Electronics 10, no. 4 (2021): 481. http://dx.doi.org/10.3390/electronics10040481.

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This paper introduces an adaptive method for detecting front vehicles under complex weather conditions. In the field of vehicle detection from images extracted by cameras installed in vehicles, backgrounds with complicated weather, such as rainy and snowy days, increase the difficulty of target detection. In order to improve the accuracy and robustness of vehicle detection in front of driverless cars, a cascade vehicle detection method combining multifeature fusion and convolutional neural network (CNN) is proposed in this paper. Firstly, local binary patterns, Haar-like and orientation gradie

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Kumar, Sourabh, and Bhaskar Kapoor Kapoor. "COMPARISON OF FACE RECOGNITION ALGORITHMS FOR SURVEILLANCE OF CHILDREN USING OPENCV." International journal of multidisciplinary advanced scientific research and innovation 1, no. 10 (2021): 328–32. http://dx.doi.org/10.53633/ijmasri.2021.1.10.013.

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Proposing a security system for surveillance of home alone children for safety purpose and send an alert to the register mobile number if some kind of intrusion is detected. I have used Viola-Jones algorithm to detect human face from the live camera and then frame is resized then resized image is processed by the Local Binary Pattern Histograms (LBPH) algorithm and save the model in a YML file and then it is implemented on live cam feed in which the algorithm will detect the face and if some unknown face has been identified it will trigger a notification to the registered mobile number using a

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Dhamale, Samidha. "A Survey on Touchless Heart Rate Measurement Using Facial Expression for Covid Patient." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 990–94. http://dx.doi.org/10.22214/ijraset.2022.39965.

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Abstract: This research describes both a touch-free heartbeat detection system and a cardiopulmonary signal modeling technique. A vector network analyzer is used to test a microwave system for detection of a heartbeat signal at a distance of 1 m from a person. The developed system can detect heartbeat signals and adjust their frequency and strength. Measurements are taken at 2.4, 5.8, 10, 16, and 60 GHz, as well as at power levels ranging from 0 to -27 dBm. Based on data for both breathing and heartbeats, a model of the recorded signals reflecting cardiopulmonary activity is provided. The hear

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Ramos, Anna Liza A., Paolo A. Buenafe, Evander Keannu C. Cabrales, Jasreel D. Teñido, and Shaina O. Portas. "Filipino based Facial Emotion Features Datasets using Haar-Cascade Classifier and Fisherfaces Linear Discriminant Analysis Algorithm." Innovatus 2, no. 1 (2019): 47–53. https://doi.org/10.5281/zenodo.5209549.

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Emotion detection is one of emerging topics in the field of research. In fact, various studies conducted utilized the available datasets – applying different methodologies and implementing the best suited algorithms to improve the classification performance and increase the recognition rate. This study aims to apply the Filipino-based facial emotion features through the revalidation of the available features in Visage Cloud API. It served as a basis in determining how the emotion differs from the expert’s validation and testing through the WEKA tool. The validation mainly checked t

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Hafidz Ubaidillah and Henny Dwi Bhakti. "Sistem Pengenalan Wajah pada Sistem KYC dengan Algoritma Local Binary Pattern Histogram." Jurnal Ilmiah Teknik Informatika dan Komunikasi 4, no. 1 (2024): 141–54. http://dx.doi.org/10.55606/juitik.v4i1.754.

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With the increasing internet usage post-pandemic, ensuring the security of a fintech application becomes imperative. Bangbeli implements KYC procedures using facial recognition technology and stringent security protocols to verify identities and safeguard users' personal data in compliance with Bank Indonesia regulations. Utilizing Haar Cascade Classifier, Local Binary Pattern Histogram, and histogram equalization, an API (Application Programming Interface) has been created for facial training and prediction. These methods were chosen for their credibility, achieving an 88% accuracy with 33 sa

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H P, Suraj. "Arduino Based Smart and Remote Voting System with Smart Card Implementation and Dual Biometric Authentication." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 3660–65. http://dx.doi.org/10.22214/ijraset.2022.45834.

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Abstract: Democracy is a synonym for progress of the nation. Improving voting numbers will boost in overall development of the country. To achieve this task, we propose an IoT based remote electronic voting system, provided with dual biometric authentication, composed of fingerprint verification and face recognition. Instead of paper based identification card, we intent to implement an RFID based smartcard with details of the voter stored in the card, including biometric data saved alongside information like name, age, locality, constituency and even aadhar identification number. This model us

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Saiteja, Kobbaji. "Healthcare Chatbot Using AI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49884.

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Abstract - In today's era, India faces a major challenge in providing affordable and accessible healthcare, especially in rural areas where transport and quality facilities are limited. To address this, we developed an AI-powered Healthcare Chatbot using Python. This system helps users get instant responses to health-related queries and locate nearby doctors, clinics, and hospitals using the Google Places API—crucial during emergencies. The project has two modules: User and Admin. Users can register, log in, manage profiles, chat with the bot, and access healthcare facility information. Admins

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Rahmad, Cahya, Nurfaidah Nurfaidah, Supriatna Adhisuwignjo, and Mamluatul Hani’ah. "Mask Detection App Uses Haar Cascade and Convolutional Neural Network to Alert Comply with Health Protocols." Applied Information System and Management (AISM) 6, no. 2 (2023): 77–82. http://dx.doi.org/10.15408/aism.v6i2.31396.

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This study aims to identify the face of a person whether wearing a mask or not wearing a mask accompanied by an appeal to the importance of wearing a mask. The contribution of this paper to science is to provide an overview of the results of accuracy, precision, recall used by the method used with data that can be accessed by many people, so that it can be developed further or can be compared. This system uses two techniques, namely the classification of whether a person is wearing a mask or not using the Convolutional Neural Network (CNN) model. The architecture used is DenseNet-12 to detect

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