Open AccessComparison of Viola-Jones Haar Cascade Classifier and Histogram of Oriented Gradients (HOG) for face detection
Author(s) -
Cahya Rahmad,
Rosa Andrie Asmara,
Dimas Rossiawan Hendra Putra,
I Gede Wahyu Surya Dharma,
Hendro Darmono,
I Muhiqqin
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/732/1/012038
Subject(s) - haar like features , artificial intelligence , face detection , pattern recognition (psychology) , cascading classifiers , computer science , histogram , classifier (uml) , adaboost , histogram of oriented gradients , viola–jones object detection framework , computer vision , sliding window protocol , facial recognition system , cascade , object class detection , image (mathematics) , window (computing) , engineering , random subspace method , operating system , chemical engineering
Human face recognition is one of the most challenging topics in the areas of image processing, computer vision, and pattern recognition. Before recognizing the human face, it is necessary to detect a face then extract the face features. Many methods have been created and developed in order to perform face detection and two of the most popular methods are Viola-Jones Haar Cascade Classifier (V-J) and Histogram of Oriented Gradients (HOG). This paper proposed a comparison between VJ and HOG for detecting the face. V-J method calculate Integral Image through Haar-like feature with AdaBoost process to make a robust cascade classifier, HOG compute the classifier for each image in and scale of the image, applied the sliding windows, extracted HOG descriptor at each window and applied the classifier, if the classifier detected an object with enough probability that resembles a face, the classifier recording the bounding box of the window and applied non-maximum suppression to make the accuracy increased. The experimental results show that the system successfully detected face based on the determined algorithm. That is mean the application using computer vision can detect face and compare the results.