A new Adaptive Approach for Histogram based Mouth Segmentation
The segmentation of mouth and lips is a fundamental
problem in facial image analyisis. In this paper we propose a method
for lip segmentation based on rg-color histogram. Statistical analysis
shows, using the rg-color-space is optimal for this purpose of a pure
color based segmentation. Initially a rough adaptive threshold selects
a histogram region, that assures that all pixels in that region are
skin pixels. Based on that pixels we build a gaussian model which
represents the skin pixels distribution and is utilized to obtain a
refined, optimal threshold. We are not incorporating shape or edge
information. In experiments we show the performance of our lip pixel
segmentation method compared to the ground truth of our dataset and
a conventional watershed algorithm.
[1] A. Al-Hamadi, A. Panning, R. Niese, and B. Michaelis. A modelbased
image analysis method for extraction and tracking of facial
features in video sequence. In The 4th International Multi-conference
on Computer Science and Information Technology CSIT 2006, Spo. by
IEEE, Amman,Vol.3, pages 499-509, 2006.
[2] S. Arca, P. Campadelli, and R. Lanzarotti. A face recognition system
based on local feature analysis. In Audio- and Video-Based Biometric
Person Authentication, pages 182-189, 2003.
[3] C. Bouvier, P.Y. Coulon, and X. Maldague. Unsupervised lips segmentation
based on roi optimisation and parametric model. In IEEE
International Conference on Image Processing, pages IV: 301-304,
2007.
[4] Jingying Chen, Bernard Tiddeman, and Gang Zhao. Advances in
Visual Computing, volume 5359/2008 of Lecture Notes in Computer
Science, chapter Real-Time Lip Contour Extraction and Tracking Using
an Improved Active Contour Model, pages 236-245. Springer Berlin /
Heidelberg, 2008.
[5] P. Cisar and Zelezny M. Using of lip-reading for speech recognition
in noisy environments. In Speech Processing, pages 137-142, Prague,
2004. Academy of Sciences of the Czech Republic.
[6] N. Eveno, A. Caplier, and P.Y. Coulon. Accurate and quasi-automatic
lip tracking. Circuits and Systems for Video Technology, 14(5):706-715,
May 2004.
[7] Erhan AliRiza Ince and Syed Amjad Ali. An adept segmentation
algorithm and its application to the extraction of local regions containing
fiducial points. In ISCIS, pages 553-562, 2006.
[8] K.S. Jang, S. Han, I. Lee, and Y.W. Woo. Lip localization based on active
shape model and gaussian mixture model. In Pacific-Rim Symposium
on Image and Video Technology, pages 1049-1058, Hsinchu , TAIWAN,
2006.
[9] J.Y. Kim, S.Y. Na, and R. Cole. Lip detection using confidence-based
adaptive thresholding. In International Symposium on Visual Computing,
pages I: 731-740, 2006.
[10] S.H. Leung, S.L. Wang, and W.H. Lau. Lip image segmentation
using fuzzy clustering incorporating an elliptic shape function. IEEE
Transaction on Image Processing, 13(1):51-62, January 2004.
[11] Trent W. Lewis and David M.W. Powers. Lip feature extraction using
red exclusion. In Peter Eades and Jesse Jin, editors, Selected papers
from Pan-Sydney Area Workshop on Visual Information Processing
(VIP2000), volume 2 of CRPIT, pages 61-67, Sydney, Australia, 2001.
ACS.
[12] D. Nguyen, D. Halupka, P. Aarabi, and A. Sheikholeslami. Real-time
face detection and lip feature extraction using field-programmable gate
arrays. IEEE Trans. Systems, Man and Cybernetics, SMC-B, 36(4):902-
912, August 2006.
[13] California Institute of Technology. Faces 1999 (front).
http://www.vision.caltech.edu/archive.html, 1999.
[14] A. Panning, A. Al-Hamadi, R. Niese, and B. Michaelis. Facial
expression recognition based on haar-like feature detection. Pattern
Recognition and Image Analysis, 18(3):447-452, 2008.
[15] Paul Viola and Michael Jones. Robust real-time object detection. Second
international workshop on statistical and computational theories of
vision modeling, learning, computing, and sampling, 2001.
[1] A. Al-Hamadi, A. Panning, R. Niese, and B. Michaelis. A modelbased
image analysis method for extraction and tracking of facial
features in video sequence. In The 4th International Multi-conference
on Computer Science and Information Technology CSIT 2006, Spo. by
IEEE, Amman,Vol.3, pages 499-509, 2006.
[2] S. Arca, P. Campadelli, and R. Lanzarotti. A face recognition system
based on local feature analysis. In Audio- and Video-Based Biometric
Person Authentication, pages 182-189, 2003.
[3] C. Bouvier, P.Y. Coulon, and X. Maldague. Unsupervised lips segmentation
based on roi optimisation and parametric model. In IEEE
International Conference on Image Processing, pages IV: 301-304,
2007.
[4] Jingying Chen, Bernard Tiddeman, and Gang Zhao. Advances in
Visual Computing, volume 5359/2008 of Lecture Notes in Computer
Science, chapter Real-Time Lip Contour Extraction and Tracking Using
an Improved Active Contour Model, pages 236-245. Springer Berlin /
Heidelberg, 2008.
[5] P. Cisar and Zelezny M. Using of lip-reading for speech recognition
in noisy environments. In Speech Processing, pages 137-142, Prague,
2004. Academy of Sciences of the Czech Republic.
[6] N. Eveno, A. Caplier, and P.Y. Coulon. Accurate and quasi-automatic
lip tracking. Circuits and Systems for Video Technology, 14(5):706-715,
May 2004.
[7] Erhan AliRiza Ince and Syed Amjad Ali. An adept segmentation
algorithm and its application to the extraction of local regions containing
fiducial points. In ISCIS, pages 553-562, 2006.
[8] K.S. Jang, S. Han, I. Lee, and Y.W. Woo. Lip localization based on active
shape model and gaussian mixture model. In Pacific-Rim Symposium
on Image and Video Technology, pages 1049-1058, Hsinchu , TAIWAN,
2006.
[9] J.Y. Kim, S.Y. Na, and R. Cole. Lip detection using confidence-based
adaptive thresholding. In International Symposium on Visual Computing,
pages I: 731-740, 2006.
[10] S.H. Leung, S.L. Wang, and W.H. Lau. Lip image segmentation
using fuzzy clustering incorporating an elliptic shape function. IEEE
Transaction on Image Processing, 13(1):51-62, January 2004.
[11] Trent W. Lewis and David M.W. Powers. Lip feature extraction using
red exclusion. In Peter Eades and Jesse Jin, editors, Selected papers
from Pan-Sydney Area Workshop on Visual Information Processing
(VIP2000), volume 2 of CRPIT, pages 61-67, Sydney, Australia, 2001.
ACS.
[12] D. Nguyen, D. Halupka, P. Aarabi, and A. Sheikholeslami. Real-time
face detection and lip feature extraction using field-programmable gate
arrays. IEEE Trans. Systems, Man and Cybernetics, SMC-B, 36(4):902-
912, August 2006.
[13] California Institute of Technology. Faces 1999 (front).
http://www.vision.caltech.edu/archive.html, 1999.
[14] A. Panning, A. Al-Hamadi, R. Niese, and B. Michaelis. Facial
expression recognition based on haar-like feature detection. Pattern
Recognition and Image Analysis, 18(3):447-452, 2008.
[15] Paul Viola and Michael Jones. Robust real-time object detection. Second
international workshop on statistical and computational theories of
vision modeling, learning, computing, and sampling, 2001.
@article{"International Journal of Electrical, Electronic and Communication Sciences:59204", author = "Axel Panning and Robert Niese and Ayoub Al-Hamadi and Bernd Michaelis", title = "A new Adaptive Approach for Histogram based Mouth Segmentation", abstract = "The segmentation of mouth and lips is a fundamental
problem in facial image analyisis. In this paper we propose a method
for lip segmentation based on rg-color histogram. Statistical analysis
shows, using the rg-color-space is optimal for this purpose of a pure
color based segmentation. Initially a rough adaptive threshold selects
a histogram region, that assures that all pixels in that region are
skin pixels. Based on that pixels we build a gaussian model which
represents the skin pixels distribution and is utilized to obtain a
refined, optimal threshold. We are not incorporating shape or edge
information. In experiments we show the performance of our lip pixel
segmentation method compared to the ground truth of our dataset and
a conventional watershed algorithm.", keywords = "Feature extraction, Segmentation, Image processing,
Application", volume = "3", number = "8", pages = "1602-6", }
