Optimalisasi Deteksi Wajah Real-Time Menggunakan HAAR Cascade Classifier berbasis OpenCV

Alfan Rizaldy Pratama (Universitas Pembangunan Nasional Veteran Jawa Timur)
Muhammad Nasrudin (Universitas Pembangunan Nasional Veteran Jawa Timur)
Andri Faudzan Adziima (Universitas Pembangunan Nasional Veteran Jawa Timur)
Shindi Shella May Wara (Universitas Pembangunan Nasional Veteran Jawa Timur)

JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer)
Vol. 7 No. 1 (2025): Juni 2025

Nowadays, the face is one of the features that is widely used in various aspects of life such as security which includes access control and surveillance, biometrics which includes attendance systems, and many others. The obstacles found in implementing this are generally about speed performance when detecting, this is vital because if the process takes a long time, misconceptions and system errors will occur. HAAR Cascade Classifier is one of the most widely used lightweight face detection algorithms. In this research, by analyzing the use of grayscale color compared to RGB, a performance increase of 6.17% is obtained with an average FPS on RGB of 25.63 while on grayscale it is 27.21.