Abstract
Detection of human skin in an arbitrary image is generally hard. Most color-based skin detection algorithms are based on a static color model of the skin. However, a static model cannot cope with the huge variability of scenes, illuminants and skin types. This is not suitable for an interacting robot that has to find people in different rooms with its camera and without any a priori knowledge about the environment nor of the lighting.
In this paper we present a new color-based algorithm called VR filter. The core of the algorithm is based on a statistical model of the colors of the pixels that generates a dynamic boundary for the skin pixels in the color space. The motivation beyond the development of the algorithm was to be able to correctly classify skin pixels in low definition images with moving objects, as the images grabbed by the omnidirectional camera mounted on the robot. However, our algorithm was designed to correctly recognizes skin pixels with any type of camera and without exploiting any information on the camera.
In the paper we present the advantages and the limitations of our algorithm and we compare its performances with the principal existing skin detection algorithms on standard perspective images.
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References
Soriano, M., Huovinen, S., Martinkauppi, B., Laaksonen, M.: Skin detection in video under changing illumination conditions. In: 15th International Conference on Pattern Recognition, vol. 1, pp. 839–842 (2000)
Martinkauppi, B.: Face colour under varying illumination - analysis and applications. PhD thesis, University of Oulu (2002)
Schwerdt, K., Crowley, J.: Robust face tracking using color (2000)
Tomaz, F., Candeias, T., Shahbazkia, H.: Improved automatic skin detection in color images. In: 7th Digital Image Computing: Techniques and Applications, pp. 419–427 (2003)
Wilhelm, T., Böhme, H.J., Gross, H.M.: A multi-modal system for tracking and analyzing faces on a mobile robot. Robotics and Autonomous Systems 48, 31–40 (2004)
Albiol, A., Torres, L., Delp, E.J.: Optimum color spaces for skin detection. In: International Conference on Image Processing, vol. 1, pp. 122–124 (2001)
Kruppa, H., Bauer, M.A., Schiele, B.: Skin patch detection in real-world images. In: Van Gool, L. (ed.) Pattern Recognition. LNCS, vol. 2449, pp. 109–117. Springer, Heidelberg (2002)
Kjeldsen, R., Kender, J.: Finding skin in color images. In: 2nd International Conference on Automatic Face and Gesture Recognition, pp. 312–317 (1996)
Lee, Y.J., Yoo, I.S.: An elliptical boundary model for skin color detection. In: International Conference on Imaging Science, Systems, and Technology, pp. 472–479
Sebe, N., Cohen, I., Huang, T.S., Gevers, T.: Skin detection: A bayesian network approach
Thrun, S.B.: Efficient exploration in reinforcement learning. Technical Report CMU-CS-92-102, Pittsburgh, Pennsylvania (1992)
Georgia Tech Laboratories: Georgia Tech Face Database, ftp://ftp.ee.gatech.edu/pub/users/hayes/facedb/
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Lastra, A., Pretto, A., Tonello, S., Menegatti, E. (2007). Robust Color-Based Skin Detection for an Interactive Robot. In: Basili, R., Pazienza, M.T. (eds) AI*IA 2007: Artificial Intelligence and Human-Oriented Computing. AI*IA 2007. Lecture Notes in Computer Science(), vol 4733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74782-6_44
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DOI: https://doi.org/10.1007/978-3-540-74782-6_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74781-9
Online ISBN: 978-3-540-74782-6
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