TY - GEN
T1 - A robust method for nose detection under various conditions
AU - Hassaballah, Mahmoud
AU - Kanazawa, Tomonori
AU - Ido, Shinobu
AU - Ido, Shun
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78049300946&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15910-7_45
DO - 10.1007/978-3-642-15910-7_45
M3 - Conference contribution
AN - SCOPUS:78049300946
SN - 3642159095
SN - 9783642159091
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 392
EP - 400
BT - Computer Vision and Graphics - International Conference, ICCVG 2010, Proceedings
T2 - International Conference on Computer Vision and Graphics, ICCVG 2010
Y2 - 20 September 2010 through 22 September 2010
ER -