Abstract
Comparison of methods in face detection is needed to provide recommendation of best method. This study compared three methods in face detection, namely OpenCV haar cascade, OpenCV Single Shot Multibox Detector (SSD) and Dlib CNN. Face detection is focused on five challenging conditions, namely face detection in head position obstacles, wearing face masks, lighting, background images that have a lot of noise, differences in expression. Data testing is taken randomly on google with reference to one image consisting of more than one detected face with wild condition. The results of the comparative analysis in wild condition show that the OpenCV haar cascade has more weaknesses with a performance percentage of 20% compared OpenCV SSD and Dlib CNN method. Performance results of SSD and Dlib CNN have the same performance in the five conditions tested, which is about 80%.
Highlights
Comparison of methods in face detection is needed to provide recommendation of best method
Face detection is focused on five challenging conditions, namely face detection in head position obstacles, wearing face masks, lighting, background images that have a lot of noise, differences in expression
The results of the comparative analysis in wild condition show that the OpenCV haar cascade has more weaknesses with a performance percentage of 20% compared OpenCV Single Shot Multibox Detector (SSD) and Dlib CNN method
Summary
SSD metode deteksi objek yang terkenal adalah single merupakan salah satu dikembangkan untuk implementasi dunia nyata daripada deteksi wajah yang terlihat dari depan dan tanpa halangan apapun atau lab controlled. Metode ini sudah Penelitian yang dilakukan sebelumnya banyak berfokus diimplementasikan pada deteksi wajah dengan halangan pada deteksi satu wajah dalam satu gambar. Penelitian sebelumnya banyak berfokus pada implementasi suatu algoritma tanpa perbandingan atau melakukan perbandingan dalam satu atau dua kondisi saja. Adapun data gambar uji pada kelima kondisi diambil secara acak di google dengan memadukan tantangan apa saja yang biasa dihadapi dalam deteksi wajah. 2.1 Alur Penelitian dengan cara menganalisis jumlah wajah yang terdeteksi dalam satu gambar. Adapun beberapa hal yang dicari adalah kinerja ketiga metode dan Kelemahan alur publikasi 3.
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