Face Detection on Infrared Thermal Image

摘要

Infrared cameras or thermal imaging cameras are devices that use infrared radiation to capture an image. This kind of sensors are being developed for almost a century now. They started to be used in the military environment, but at that time it took too long to create a single image. Nowadays, the infrared sensors have reached a whole new technological level and are used for purposes other than military ones, as happens in this work, where they are being used for face detection. When comparing the use of thermal images regarding color images, it is possible to see some advantages and some limitations, which will be explored in this paper. This work proposes the development or adaptation of several methods for face detection on infrared thermal images. The well known algorithm developed by Paul Viola and Michael Jones, using Haar feature-based cascade classifiers, is used to compare the traditional algorithms developed for visible light images when applied to thermal imaging. In this paper, we present three different methods for face detection. As far as we know, there is limited research on this topic so we think this work is an important contribution to the field. In the first one, an edge detection algorithm is applied to the binary image and the face detection is based on these contours. In the second method, a template matching method is used for searching and finding the location of a template image with the shape of human head in the binary image. In the last one, a matching algorithm is used. This algorithm correlates a template with the distance transform of the edge image. This algorithm incorporates edge orientation information resulting in the reduction of false detection and the cost variation is limited. The results show that the proposed methods have promising outcome, but the second method is the most suitable for the performed experiments.