Penerapan Face Recognition pada Aplikasi Akademik Online

Abstract

In the era of big data, the biometric identification process is growing very fast and is increasingly being implemented in many applications. Face recognition technology utilizes artificial intelligence (AI) to recognize faces that are already stored in the database. In this research, it is proposed to design an online academic login system at the National University using real time face recognition used OpenCV with the Local Binary Pattern Histogram algorithm and the Haar Cassade Classification method. The system will detect, recognize and compare faces with the stored face database. The image used is 480 x 680 pixels with a .jpg extension in the form of an RGB image which will be converted into a Grayscale image., to make it easier to calculate the histogram value of each face that will be recognized. With a modeling system like this it is hope to make it easy for user to log into online academics.Keywords:Face Recognition, Haar Cascade Clasifier, Local Binary Pattern Histogram, Online Akademic, OpenCV.

Attendance Mobile Application With Face Recognition and Detect Location

“…Face recognition using the Local Binary Pattern Histogram method and the Haar Cascade Classifier method, based on the results of the experiments conducted, shows that the application of the above two methods can be used in combination to detect and recognize faces in real time for the needs of online enrollment in higher education when the lighting conditions are sufficient and the face is not obscured by other objects. [12].…”

Attendance Mobile Application With Face Recognition and Detect Location

“…Face recognition using the Local Binary Pattern Histogram method and the Haar Cascade Classifier method, based on the results of the experiments conducted, shows that the application of the above two methods can be used in combination to detect and recognize faces in real time for the needs of online enrollment in higher education when the lighting conditions are sufficient and the face is not obscured by other objects. [12].…”

Potensi Sistem Absensi Berdasarkan Citra Wajah Sebagai Media Pengabdian Kepada Masyarakat

“…Wajah yang dikenali akan dibandingkan dengan wajah yang tidak dikenali pada data yang sudah tersimpan. Pengenalan wajah terdiri dari tiga bagian seperti segmentasi wajah, ekstraksi wajah, dan pengenalan wajah [14]. Sistem menggunakan pengenalan wajah berdasarkan camera, scan wajah dengan intesitas cahaya, acquisition data, preprocessing, face database, training sets, classification as "known" and "unknown", dan hasil scan wajah.…”

Attendance Mobile Application With Face Recognition and Detect Location

“…Face recognition using the Local Binary Pattern Histogram method and the Haar Cascade Classifier method, based on the results of the experiments conducted, shows that the application of the above two methods can be used in combination to detect and recognize faces in real time for the needs of online enrollment in higher education when the lighting conditions are sufficient and the face is not obscured by other objects. [12].…”

Potensi Sistem Absensi Berdasarkan Citra Wajah Sebagai Media Pengabdian Kepada Masyarakat

“…Wajah yang dikenali akan dibandingkan dengan wajah yang tidak dikenali pada data yang sudah tersimpan. Pengenalan wajah terdiri dari tiga bagian seperti segmentasi wajah, ekstraksi wajah, dan pengenalan wajah [14]. Sistem menggunakan pengenalan wajah berdasarkan camera, scan wajah dengan intesitas cahaya, acquisition data, preprocessing, face database, training sets, classification as "known" and "unknown", dan hasil scan wajah.…”

Attendance Mobile Application With Face Recognition and Detect Location

“…Face recognition using the Local Binary Pattern Histogram method and the Haar Cascade Classifier method, based on the results of the experiments conducted, shows that the application of the above two methods can be used in combination to detect and recognize faces in real time for the needs of online enrollment in higher education when the lighting conditions are sufficient and the face is not obscured by other objects. [12].…”

Potensi Sistem Absensi Berdasarkan Citra Wajah Sebagai Media Pengabdian Kepada Masyarakat

“…Wajah yang dikenali akan dibandingkan dengan wajah yang tidak dikenali pada data yang sudah tersimpan. Pengenalan wajah terdiri dari tiga bagian seperti segmentasi wajah, ekstraksi wajah, dan pengenalan wajah [14]. Sistem menggunakan pengenalan wajah berdasarkan camera, scan wajah dengan intesitas cahaya, acquisition data, preprocessing, face database, training sets, classification as "known" and "unknown", dan hasil scan wajah.…”