Detection of the side faces of the image (part two)

Abstract

As the appearance of a person had always been the primary identity, many had researched and introduced different ways to identify people. That includes fingerprint identification and handprint identification. Face detection is one of the

ways to identify a human face in images. It had since improved and used in multiple

applications such as person identification and surveillance. Though the side face

detection have not been able to live up to the expectation, it will be beneficial to

implement side face detection on applications for criminal identification and image annotation. This project focuses on using the Viola-Jones method to detect side-view faces and analyses the results that will be obtain. The algorithm uses the Haar-basis functions to get features to be learned in the AdaBoost learning algorithm. It is implemented for the selection of features. The features then will be used to go through classifiers. C++ Computing language and OpenCV is chosen as the platform to analyze the side face detection.