Sliding window protocol

Random subspace method

Pattern recognition (psychology)

Operating system

Image (mathematics)

Haar-like features

Engineering

AdaBoost

Histogram of oriented gradients

Artificial intelligence

Viola–Jones object detection framework

Window (computing)

Facial recognition system

Cascade

Cascading classifiers

Computer science

Histogram

Computer vision

Chemical engineering

Face detection

Object-class detection

Classifier (UML)

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.

Comparison of Viola-Jones Haar Cascade Classifier and Histogram of Oriented Gradients (HOG) for face detection