Abstract
In this paper, a robust fully automatic method for nose field detection under different imaging conditions is presented. It depends on the local appearance and shape of nose region characterized by edge information. Independent Components Analysis (ICA) is used to learn the appearance of nose. We show experimentally that using edge information for characterizing appearance and shape outperforms using intensity information. The influence of preprocessing step on the performance of the method is also examined. A subregion-based framework depending on statistical analysis of intensity information in the nose region is proposed to improve the efficiency of ICA. Experimental results show that the proposed method can accurately detect nose with an average detection rate of 95.5 % on 6778 images from six different databases without prior detection for other facial features, outperforming existing methods.
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Hassaballah, M., Kanazawa, T., Ido, S., Ido, S. (2010). A Robust Method for Nose Detection under Various Conditions. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2010. Lecture Notes in Computer Science, vol 6374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15910-7_45
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DOI: https://doi.org/10.1007/978-3-642-15910-7_45
Publisher Name: Springer, Berlin, Heidelberg
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