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accuracy with 60% noise intensity. In the case of
Gaussian noise, the results fluctuate with noise
variance. For dataset A, the system gives 100%
accuracy up to 2% noise variance, while it fluctuates
between 0 and 100 up to 6% variance, while for
dataset B the accuracy fluctuates between 0 and 100
up to 4% noise variance and between 0 to 30 up to
6% variance. Similarly, in the case of Speckle noise,
the results fluctuate with noise variance. For dataset
A, the system gives 100% accuracy up to 2% noise
variance, while it fluctuates between 90-95% up to
10% variance, while for dataset B the accuracy
fluctuates between 85 and 100 up to 5% noise
variance and between 75 to 100 up to 9% variance.
5 CONCLUSIONS
This paper has presented a hybrid system for human
face detection from static images, by combining two
methods and some pre-processing steps that is more
efficient than either and not too much less
computationally efficient than the better of the two.
We have shown that it is fairly robust to common
image problems such as noise and occlusions.
A brief review of the literature is presented along
with the most comprehensive set of RGB images
which fulfils all possible conditions for evaluation of
any face related application. The proposed system is
not complex and covers a wide range of human skin
colours.
We intend to follow two fronts of research from
here. The first is to use these results and extend them
to video images, which will combine tracking with
face detection. We will investigate further
algorithmic speed-ups for this to work in real time.
The second is to use the segmented face for human
emotion recognition which is the main focus of our
research.
REFERENCES
Anisetti, M., Bellandi, V., Damiani, E., Beverina, F.,
Arnone, L. & Rat, B. 2006, ‘A3fd: Accurate 3d face
detection’, Proc. of IEEE Int'l. Conf. on Signal-Image
Technology and Internet Based Systems.
Asteriadis, S., Nikolaidis, N., Pitas, I. 2009, ‘Facial feature
detection using distance vector fields’, Pattern
Recognition,1388-1398.
Garcia, C., Delakis, M. 2004, ‘Convolutional face finder:
A neural architecture for fast and robust face
detection’, IEEE Trans on Pattern Analysis and
Machine Intelligence, 1408–1423.
Hjelmas, E., Low, B. K. 2001, ‘Face Detection: A
Survey’, Computer Vision and Image Understanding,
236-274.
Hsu, R., Abdel-Mottaleb, M., Jain, A. K. May 2002, ‘Face
Detection in Colour Images’, IEEE Trans. on Pattern
Analysis and Machine Intelligence.
Jain, V., Mukherjee, A. 2002, ‘The Indian Face Database’.
Lee, T., Park, S., Park, M. 2005, ‘A New Facial Features
and Face Detection Method for Human-Robot
Interaction’, Proc. of the 2005 IEEE Int'l. Conf. on
Robotics and Automation, 2063-2068.
Lienhart, R., Maydt, J. 2002, ‘An Extended Set of Haar-
like Features for Rapid Object Detection’, IEEE ICIP.
Lin, H., Yen, S., Yeh, J., Lin, M. 2008, ‘Face Detection
Based on Skin Colour Segmentation and SVM
Classification’, Second Int'l. Conf. on Secure System
Integration and Reliability Improvement, 230-231.
Martinez, A. M., Benavente, R. 1998, The AR Face
Database. CVC Technical Report #24.
McDermott, J. 2006, ‘Facial Feature Extraction for Face
Characterization’.
Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.
1999, ‘XM2VTSDB: the extended M2VTS database’.
Pantic, M., Valstar, M. F., Rademaker, R., Maat, L. 2005,
‘Web-based Database for Facial Expression Analysis’,
Proc. IEEE Int'l Conf. Multmedia and Expo.
Peer, P., Solina, F. 1999, ‘An Automatic Human Face
Detection Method’, Proc. of the 4th Computer Vision
Winter Workshop (CVWW’99), 122–130.
Singh, S. Kr., Chauhan, D. S., Vatsa, M., Singh, R. 2003,
‘A Robust Skin Colour Based Face Detection
Algorithm’, Tamkang Journal of Science and
Engineering, 227-234.
Spacek, L., Facial images dataset.
Viola, P., Jones, M. J. 2004, ‘Robust real-time object
detection’, Int'l Journal of Computer Vision, 137-154.
Wang, J. G., Sung, E. 1999, ‘Frontal view face detection
and facial feature extraction using color and
morphological operations’, Pattern Recognition, 1053-
1068.
Wang, Y. Yuan, B. 2001, ‘A novel approach for human
face detection from colour images under complex
background’, Pattern Recognition, 1983-1992.
Yang, M., Kreigman, J., Ahuja, N. 2002, ‘Detecting Faces
in Images: A Survey’, IEEE Trans. On Pattern
Analysis and Machine Intelligence.
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