The use of thermal images of a selected area of the head in screening systems, which perform fast and accurate analysis of the temperature distribution of individual areas, requires the use of profiled image analysis methods. There exist methods for automated face analysis which are used at airports or train stations and are designed to detect people with fever. However, they do not enable automatic separation of specific areas of the face. This paper presents an algorithm for image analysis which enables localization of characteristic areas of the face in thermograms. The algorithm is resistant to subjects' variability and also to changes in the position and orientation of the head. In addition, an attempt was made to eliminate the impact of background and interference caused by hair and hairline. The algorithm automatically adjusts its operation parameters to suit the prevailing room conditions. Compared to previous studies (Marzec et al., J Med Inform Tech 16:151-159, 2010), the set of thermal images was expanded by 34 images. As a result, the research material was a total of 125 patients' thermograms performed in the Department of Pediatrics and Child and Adolescent Neurology in Katowice, Poland. The images were taken interchangeably with several thermal cameras: AGEMA 590 PAL (sensitivity of 0.1°C), ThermaCam S65 (sensitivity of 0.08°C), A310 (sensitivity of 0.05°C), T335 (sensitivity of 0.05°C) with a 320×240 pixel optical resolution of detectors, maintaining the principles related to taking thermal images for medical thermography. In comparison to (Marzec et al., J Med Inform Tech 16:151-159, 2010), the approach presented there has been extended and modified. Based on the comparison with other methods presented in the literature, it was demonstrated that this method is more complex as it enables to determine the approximate areas of selected parts of Multimed Tools Appl (2015)
The paper presents a fast algorithm for eye localization from thermal images. Due to blurred edges, lower quality of thermal images compared with visible-light images, the process may be more complicated. On the other hand, it seems relatively easy to designate specific areas of the eyes because of their relatively highest brightness resulting from the highest temperature in these face areas. It turns out, however, that the highest pixel brightness does not always determine unequivocally the position of the eyes. Therefore, the algorithm described here proposes a specialized method for segmentation of images containing eye areas and a specialized classifier. The first part of the algorithm is a block for generating features describing eye areas. The second stage is a decision block built based on a neural network allowing for effective classification of pre-designated areas. A suitable combination of these blocks into a single system allowed for correct analysis of images with rich geometric diversity (scale, position, orientation) and brightness distribution, reaching more than 91 % of correct localizations and time of analysis of a single image of hundredths of a second. Algorithms of this type can be used as the initial stages of facial recognition, emotion recognition, quantitative analysis of the face or modern human-computer interfaces. The described algorithm may be used for both conventional thermal images and thermograms (after prior conversion of temperature values into brightness values), which affects its potential application in medicine. The benefits of this study are twofold: fast localization of characteristic points of the eyes and the possibility of processing despite the typical problems of thermovision and image transformations.
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