A Study of Various Face Detection Methods

Abstract: Because of image-databases and ―live‖ video information is growing more and more widespread, their intelligent or automatic examining is becoming exceptionally important. People, i.e. human faces, are one of most common and very specific objects, that we try to trace in images. Face detection is a difficult task in image analysis which has each day more and more applications. We can define the face detection problem as a computer vision task which consists in detecting one or several human faces in an image. It is one of the first and the most important steps of Face analysis. In this paper we presented various methods of face detection, which are commonly used. The seminal Viola-Jones face detector is first reviewed. We after that survey a variety of techniques according to how they extract features and what learning algorithms are adopted. These methods are Local Binary Pattern (LBP), Adaboost algorithm, SMQT Features and SNOW Classifier Method and Neural Network-Based Face Detection. It is our hope that by reviewing the numerous existing algorithms, we will see yet better algorithms developed to solve this fundamental computer vision problem. In this survey, we categorize the detection methods on the basis of the object and motion representations used, present thorough descriptions of representative methods in each category, and look at their pros and cons.

Keywords: Face detection, Viola-Jones face detector, Local Binary Pattern (LBP), Adaboost algorithm, SMQT Features and SNOW Classifier, Neural Network-Based Face Detection.