Open AccessMulti-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images
Author(s) -
R. Rizal Isnanto,
Adian Fatchur Rochim,
Dania Eridani,
Guntur Cahyono
Publication year - 2021
Publication title -
international journal of engineering and technology innovation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 8
eISSN - 2226-809X
pISSN - 2223-5329
DOI - 10.46604/ijeti.2021.6174
Subject(s) - artificial intelligence , histogram , local binary patterns , computer vision , pattern recognition (psychology) , histogram equalization , haar like features , pixel , computer science , cascading classifiers , facial recognition system , classifier (uml) , face detection , image (mathematics) , random subspace method
This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%.