Relevant bibliographies by topics / Haarcascade Classifier

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Academic literature on the topic 'Haarcascade Classifier'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Haarcascade Classifier"

1

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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Conference papers on the topic "Haarcascade Classifier"

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Burnwal, Harsh Kumar, Muskan Mishra, and K. Annapurani. "Proposed Music Mapping Algorithm Based on Human Emotions." In International Research Conference on IOT, Cloud and Data Science. Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-p00umt.

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Facial recognition based music system plays an important role in the treatment of human psychology. Face recognition system is an extensively used technique in most of the applications such as security system, video processing, in surveillance system and so on. People are often confused while choosing the kind of music they would want to listen. Relatively, this paper focuses on making an efficient music recommendation system which will recommend a suitable music to make the person feel sooth using Facial Recognition Techniques. This system uses FER-2013 dataset for training of the CNN, which is made using mini-xception architecture. Augmentation techniques are used for increasing the number of images in the dataset for training, which helps to increase the accuracy of the prediction. The face is captured using webcam and facial extraction is done using Haarcascade classifier and then sent to the CNN layers. The mini xception algorithm used in these CNN layers makes the system lighter and efficient as compared to existing systems. The accuracy of the proposed model is calculated and found to have reached the barrier threshold of 95% and average accuracy was found to be 90%. The song is recommended to the user using the proposed mapping algorithm.
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Hashmi, Nauman Zafar, Aun Irtaza, Wakeel Ahmed, and Nudrat Nida. "An augmented reality based Virtual dressing room using Haarcascades Classifier." In 2020 14th International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2020. http://dx.doi.org/10.1109/icosst51357.2020.9333032.

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