A New Face Detection Technique using 2D DCT and Self Organizing Feature Map
This paper presents a new technique for detection of
human faces within color images. The approach relies on image
segmentation based on skin color, features extracted from the two-dimensional
discrete cosine transform (DCT), and self-organizing
maps (SOM). After candidate skin regions are extracted, feature
vectors are constructed using DCT coefficients computed from those
regions. A supervised SOM training session is used to cluster feature
vectors into groups, and to assign “face" or “non-face" labels to those
clusters. Evaluation was performed using a new image database of
286 images, containing 1027 faces. After training, our detection
technique achieved a detection rate of 77.94% during subsequent
tests, with a false positive rate of 5.14%. To our knowledge, the
proposed technique is the first to combine DCT-based feature
extraction with a SOM for detecting human faces within color
images. It is also one of a few attempts to combine a feature-invariant
approach, such as color-based skin segmentation, together with
appearance-based face detection. The main advantage of the new
technique is its low computational requirements, in terms of both
processing speed and memory utilization.
[1] E. Hjelmås, and B. K. Low, "Face detection: a survey", Computer Vision
and Image Understanding, Vol. 83, No. 3, Sept. 2001, pp. 236-274.
[2] M. H. Yang, D. J. Kriegman, and N. Ahuja, "Detecting faces in images:
a survey", IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 24, No. 1, Jan. 2002, pp. 34 - 58.
[3] V. Vezhnevets, V. Sazonov, and A. Andreeva, "A survey on pixel-based
skin color detection techniques", in Proc. Graphicon-2003.
[4] H. Kruppa, M. A. Bauer, and B. Schiele, "Skin patch detection in real world
images", in Proc. of the DAGM-Symposium, 2002, pp. 109-116.
[5] M.-H. Yang and N. Ahuja, "Detecting human faces in color images," in
Proc. of the International Conference on Image Processing, vol. 1, pp.
127-130, 1998.
[6] B. Jedynak, H. Zheng, M. Daoudi, and D. Barret, "Maximum entropy
models for skin detection," IRMA Technical Report, Vol. 57, No. XIII,
Universite des Sciences et Technologies de Lille, France, 2002.
[7] R. C. Gonzalez and R. E. Woods, Digital Image Processing. Prentice
Hall, 2002.
[8] E. Ifeachor and B. Jervis, Digital Signal Processing: A Practical
Approach. Prentice Hall, 2001.
[9] L. Ma, Y. Xiao, K. Khorasani, and R. K. Ward, "A new facial expression
recognition technique using 2D DCT and k-means algorithm", in Proc.
International Conference on Image Processing, Oct. 2004, pp. 1269-
1272.
[10] L. Ma and K. Khorasani, "Facial expression recognition using
constructive feedforward neural networks", IEEE Transactions on
Systems, Man and Cybernetics, Part B, Vol. 34, No. 3, June 2004, pp.
1588 - 1595.
[11] T. Kohonen, Self-Organizing Maps. Springer, Berlin, Heidelberg, 1995.
[12] J. Vesanto, J. Himberg, E. Alhoniemi, and J. Parhankangas," Selforganizing
map in Matlab: the SOM Toolbox", in Proc. of Matlab DSP
Conference, Espoo, Finland, November 1999, pp. 30-40.
[13] D. Brown, I. Craw, and J. Lewthwaite, "A SOM based approach to skin
detection with application in real time systems", in Proc. of the British
Machine Vision Conference, 2001.
[14] A. Abdallah, M. Abou El-Nasr, and A. Lynn Abbott, "A new color
image database for benchmarking of automatic face detection and human
skin segmentation techniques", to appear in Proc. of the Fourth
International Conference on Machine Learning and Pattern Recognition
(MLPR 2007), Barcelona, Spain, Apr. 2007.
[1] E. Hjelmås, and B. K. Low, "Face detection: a survey", Computer Vision
and Image Understanding, Vol. 83, No. 3, Sept. 2001, pp. 236-274.
[2] M. H. Yang, D. J. Kriegman, and N. Ahuja, "Detecting faces in images:
a survey", IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 24, No. 1, Jan. 2002, pp. 34 - 58.
[3] V. Vezhnevets, V. Sazonov, and A. Andreeva, "A survey on pixel-based
skin color detection techniques", in Proc. Graphicon-2003.
[4] H. Kruppa, M. A. Bauer, and B. Schiele, "Skin patch detection in real world
images", in Proc. of the DAGM-Symposium, 2002, pp. 109-116.
[5] M.-H. Yang and N. Ahuja, "Detecting human faces in color images," in
Proc. of the International Conference on Image Processing, vol. 1, pp.
127-130, 1998.
[6] B. Jedynak, H. Zheng, M. Daoudi, and D. Barret, "Maximum entropy
models for skin detection," IRMA Technical Report, Vol. 57, No. XIII,
Universite des Sciences et Technologies de Lille, France, 2002.
[7] R. C. Gonzalez and R. E. Woods, Digital Image Processing. Prentice
Hall, 2002.
[8] E. Ifeachor and B. Jervis, Digital Signal Processing: A Practical
Approach. Prentice Hall, 2001.
[9] L. Ma, Y. Xiao, K. Khorasani, and R. K. Ward, "A new facial expression
recognition technique using 2D DCT and k-means algorithm", in Proc.
International Conference on Image Processing, Oct. 2004, pp. 1269-
1272.
[10] L. Ma and K. Khorasani, "Facial expression recognition using
constructive feedforward neural networks", IEEE Transactions on
Systems, Man and Cybernetics, Part B, Vol. 34, No. 3, June 2004, pp.
1588 - 1595.
[11] T. Kohonen, Self-Organizing Maps. Springer, Berlin, Heidelberg, 1995.
[12] J. Vesanto, J. Himberg, E. Alhoniemi, and J. Parhankangas," Selforganizing
map in Matlab: the SOM Toolbox", in Proc. of Matlab DSP
Conference, Espoo, Finland, November 1999, pp. 30-40.
[13] D. Brown, I. Craw, and J. Lewthwaite, "A SOM based approach to skin
detection with application in real time systems", in Proc. of the British
Machine Vision Conference, 2001.
[14] A. Abdallah, M. Abou El-Nasr, and A. Lynn Abbott, "A new color
image database for benchmarking of automatic face detection and human
skin segmentation techniques", to appear in Proc. of the Fourth
International Conference on Machine Learning and Pattern Recognition
(MLPR 2007), Barcelona, Spain, Apr. 2007.
@article{"International Journal of Information, Control and Computer Sciences:61218", author = "Abdallah S. Abdallah and A. Lynn Abbott and Mohamad Abou El-Nasr", title = "A New Face Detection Technique using 2D DCT and Self Organizing Feature Map ", abstract = "This paper presents a new technique for detection of
human faces within color images. The approach relies on image
segmentation based on skin color, features extracted from the two-dimensional
discrete cosine transform (DCT), and self-organizing
maps (SOM). After candidate skin regions are extracted, feature
vectors are constructed using DCT coefficients computed from those
regions. A supervised SOM training session is used to cluster feature
vectors into groups, and to assign “face" or “non-face" labels to those
clusters. Evaluation was performed using a new image database of
286 images, containing 1027 faces. After training, our detection
technique achieved a detection rate of 77.94% during subsequent
tests, with a false positive rate of 5.14%. To our knowledge, the
proposed technique is the first to combine DCT-based feature
extraction with a SOM for detecting human faces within color
images. It is also one of a few attempts to combine a feature-invariant
approach, such as color-based skin segmentation, together with
appearance-based face detection. The main advantage of the new
technique is its low computational requirements, in terms of both
processing speed and memory utilization.", keywords = "Face detection, skin color segmentation, self-organizingmap.", volume = "1", number = "3", pages = "697-5", }