Enhanced Face Detection Technique Based on Color Correction Approach and SMQT Features

Mohamed A. El-Sayed, Nora G. Ahmed

Department of Mathematics, Faculty of Science, Fayoum University, Al Fayoum, Egypt.
Department of Mathematics, Faculty of Science, Sohag University, Sohag, Egypt.

DOI: 10.4236/jsea.2013.610062                

Abstract

Face detection is considered as a challenging problem in the field of image analysis and computer vision. There are many researches in this area, but because of its importance, it needs to be further developed. Successive Mean Quantization Transform (SMQT) for illumination and sensor insensitive operation and Sparse Network of Winnow (SNoW) to speed up the original classifier based face detection technique presented such a good result. In this paper we use the Mean of Medians of CbCr (MMCbCr) color correction approach to enhance the combined SMQT features and SNoW classifier face detection technique. The proposed technique is applied on color images gathered from various sources such as Internet, and Georgia Database. Experimental results show that the face detection performance of the proposed method is more effective and accurate compared to SFSC method.

Keywords

Face Detection; Color Correction; MMCbCr; SMQT Features

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Y. Ming-Hsuan, K. David and A. Narendra, “Detecting Faces in Images: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 1, 2002, pp. 34-58. http://dx.doi.org/10.1109/34. 982883
[2]	I. Kim, J. Hyung Shim and J. Yang, “Face Detection,” Stanford University, 2010. http://www.stanford.edu/class/ee368/Project_03/Project/reports/ee368group02. pdf
[3]	M. A. El-Sayed and N. Aboelwafa, “Study of Face Recognition Approach Based on Similarity Measures,” International Journal of Computer Science Issues (IJCSI), Vol. 9, No. 2, 2012, pp. 133-139.
[4]	M. A. El-Sayed and M. A. Khafagy, “An Identification System Using Eye Detection Based on Wavelets and Neural Networks,” International Journal of Computer and Information Technology, Vol. 1, No. 2, 2012, pp. 43-48.
[5]	M. A. El-Sayed, “Edges Detection Based on Renyi Entropy with Split/Merge,” Computer Engineering and Intelligent Systems (CEIS), Vol. 3, No. 9, 2012, pp. 32-41.
[6]	M. Nilsson, J. Nordberg and I. Claesson, “Face Detection Using Local SMQT Features and Split up SNOW Classifier,” IEEE International conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 2, 2007, pp. 589-592.
[7]	W. Kienzle, G. Bakir, M. Franz and B. Scholkopf, “Face Detection—Efficient and Rank Deficient,” In: Y. Weiss, Ed., Advances in Neural Information Processing Systems, Vol. 17, MIT Press, Cambridge, 2005, pp. 673-680.
[8]	Z. Shaaban, “Face Detection Methods,” World Scientific and Engineering Academy and Society (WSEAS), 2011.
[9]	J. Wu and Z.-H. Zhou, “Efficient Face Candidates Selector for Face Detection,” Pattern Recognition, Vol. 36, No. 5, 2003, pp. 1175-1186. http://dx.doi.org/10.1016/S0031-3203(02)00165-6
[10]	H. A. Rowley, S. Baluja and T. Kanade, “Neural Network-Based Face Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, 1998, pp. 23-28. http://dx.doi.org/10.1109/ 34.655647
[11]	J. Yin and J. R. Cooperstock, “Color Correction Methods with Applications to Digital Projection Environments,” Journal of WSCG, 2004, in press.
[12]	M. A. Berbar, “Novel Colors Correction Approaches for Natural Scenes and Skin Detection Techniques,” International Journal of Video & Image Processing and Network Security IJVIPNS-IJENS, Vol. 11, No. 2, 2011, pp. 1-10.
[13]	E. Prathibha, A. Manjunath and R Likitha, “RGB to YCbCr Color Conversion Using VHDL Approach,” International Journal of Engineering Research and Development, Vol. 1, No. 3, 2012, pp. 15-22.
[14]	B. Froba and A. Ernst, “Face Detection with the Modified Census Transform,” 6th IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, 17-19 May 2004, pp. 91-96.
[15]	P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, 2001, pp. 511-518.