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.
Highlights
Face detection is a computer technology that determines the locations and sizes of human faces in digital images
For face detection we use (SFSC) method: local Successive Mean Quantization Transform (SMQT) features which can be used as feature extraction for object and Sparse Network of Winnow (SNoW) classifier require for training
The proposed method is applied on 150 color images gathered from various sources such as Internet, UCD Face Image Database and Georgia Database
Summary
Face detection is a computer technology that determines the locations and sizes of human faces in digital images. These differences complicate the task of computer vision applications involving the use of more than one camera. For face detection we use (SFSC) method: local SMQT features which can be used as feature extraction for object and SNoW classifier require for training. We found that we can enhance this method by applying MMCbCr Color Correction approach on the input images that make the process of face detection better.
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