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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.

has been cited by the following article:

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

  AUTHORS: Mohamed A. El-Sayed, Nora G. Ahmed

  KEYWORDS: Face Detection; Color Correction; MMCbCr; SMQT Features

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.6 No.10, October 4, 2013

  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.