Techniques for Skin, Face, Eye and Lip Detection using Skin Segmentation in Color Images

Techniques for Skin, Face, Eye and Lip Detection using Skin Segmentation in Color Images

Mohammadreza Hajiarbabi (Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USA) and Arvin Agah (Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USA)
Copyright: © 2015 |Pages: 23
DOI: 10.4018/IJCVIP.2015070103
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Face detection is a challenging and important problem in Computer Vision. In most of the face recognition systems, face detection is used in order to locate the faces in the images. There are different methods for detecting faces in images. One of these methods is to try to find faces in the part of the image that contains human skin. This can be done by using the information of human skin color. Skin detection can be challenging due to factors such as the differences in illumination, different cameras, ranges of skin colors due to different ethnicities, and other variations. Neural networks have been used for detecting human skin. Different methods have been applied to neural networks in order to increase the detection rate of the human skin. The resulting image is then used in the detection phase. The resulting image consists of several components and in the face detection phase, the faces are found by just searching those components. If the components consist of just faces, then the faces can be detected using correlation. Eye and lip detections have also been investigated using different methods, using information from different color spaces. The speed of face detection methods using color images is compared with other face detection methods.
Article Preview

2. Face Detection Techniques

Face detection can be accomplished using color images or gray-scale images. Two popular methods in face detection are Rowley et al. (1998) and Viola- Jones (2001) methods.

Rowley et al. used neural networks and it detects upright frontal faces in gray-scale images (1998). For detecting faces a 20*20 window is used which moves pixel by pixel on the image. The extracted sub image is then applied to a receptive field neural network in order to decide if the sub image contains a face. The image is subsampled several times in order to find faces with sizes less than 20*20.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 7: 4 Issues (2017): 3 Released, 1 Forthcoming
Volume 6: 2 Issues (2016)
Volume 5: 2 Issues (2015)
Volume 4: 2 Issues (2014)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing