Detection of Face Direction by Implementing Face Edge Patterns

Abstract

In this paper, a method for detecting the direction of a human face is developed; regardless of its age or sex. The method involves creating a set of five face patterns representing the front, up, down, left, and right directions of a face. The face patterns are produced by applying Canny’s edge detection algorithm on some face files. The direction of the input face is found by first applying the above algorithm on the input file and comparing it with the five face patterns. The face pattern that gives minimum difference will represent the direction of the input face. Excellent results were reported when applied on images with relatively clear background and the head were centered at the image area. 1. Introduction The interface between people and computers has developed over the years from the early days of switches and LEDs to punched cards, interactive command-line interfaces, and the direct manipulation style of graphical user interfaces (GUI). The “desktop metaphor” of graphical user interfaces, also known as WIMP interfaces (for Windows, Icons, Menus, and Pointing devices), has been the standard interface between people and computers for many years. Of course, software and technology for humancomputer interaction (HCI) is not isolated from other aspects of computing. In addition, there are many non-GUI (or “post-WIMP”) technologies, such as virtual reality, speech recognition, computer vision, and spatial sound, which promise to change the status quo in computer-human interaction [1]. Human face detection plays an important role in many applications such as intelligent human computer interface (HCI), biometric identification, and face recognition. The goal of any face detection technique is to identify the face regions within a given image. The reliable detection of faces has been an ongoing research topic for decades [2]. Current face detection methods can be categorized into image-based methods and feature-based methods. Image-based methods use the intensity array as template to match the standardized candidate regions. Feature-based methods detect facial features using the skin color, the specific patterns of various facial features, and their relative positions [3].