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. 2013 Sep:60:225-234.
doi: 10.1016/j.infrared.2013.05.007. Epub 2013 May 24.

Face and eyes localization algorithm in thermal images for temperature measurement of the inner canthus of the eyes

Sebastian Budzan  1 , Roman Wyżgolik  1
Affiliations

Face and eyes localization algorithm in thermal images for temperature measurement of the inner canthus of the eyes

Sebastian Budzan et al. Infrared Phys Technol. 2013 Sep.

Abstract

In this paper, a novel algorithm for the detection and localization of the face and eyes in thermal images is presented, particularly the temperature measurement of the human body by measuring the eye corner (inner canthus) temperature. The algorithm uses a combination of the template-matching, knowledge-based and morphological methods, particularly the modified Randomized Hough Transform (RHT) in the localization process, also growing segmentation to increase accuracy of the localization algorithm. In many solutions, the localization of the face and/or eyes is made by manual selection of the regions of the face and eyes and then the average temperature in the region is measured. The paper also discusses experimental studies and the results, which allowed the evaluation of the effectiveness of the developed algorithm. The standardization of measurement, necessary for proper temperature measurement with the use of infrared thermal imaging, are also presented.

Keywords: Face localization; Feature extraction; Image processing; Thermal imaging.

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Figures

Fig. 1
Fig. 1
Thermal image of the face with increased core temperature.
Fig. 2
Fig. 2
Examples of common problems: (a) thermal image is taken too close, (b) thermal image is taken too far away, (c) face is distorted by cream (right side of the face) and (d) wearing spectacles prevents correct temperature measurement.
Fig. 3
Fig. 3
Scheme of the measuring stand for the investigation of the metrological properties of the ThermoPro TP8 thermal imaging camera.
Fig. 4
Fig. 4
(a) Results of stability test for the TP8 thermal imaging camera, (b) results of temperature error in measured range.
Fig. 5
Fig. 5
Influence of object in scene on measured temperature of the object (black body cavity in this case).
Fig. 6
Fig. 6
Scheme of the proposed algorithm.
Fig. 7
Fig. 7
Estimate of ellipse center.
Fig. 8
Fig. 8
Scheme of the face and eye localization part of the proposed algorithm.
Fig. 9
Fig. 9
Scheme of the examination room.
Fig. 10
Fig. 10
Examples of thermal face images. Raw thermal image (left). Normalized thermal image (right).
Fig. 11
Fig. 11
Thermal face image from different distances camera-face. 0.5 m, 1.5 m, 3 m (from left to right).
Fig. 12
Fig. 12
Relationship between average temperature of the eye region and the distance from the infrared thermal imager. Also the temperature of the human body measured by the contact method is presented on the chart.
Fig. 13
Fig. 13
Comparison of the average number of pixels in regions using the proposed algorithm.
Fig. 14
Fig. 14
Input thermal image (a), image after normalization (b), image with face and eyes ellipses (c) and image with checked inner canthus areas (d).
See this image and copyright information in PMC

References

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