An Effective Face Detection Algorithm Based on Skin Color Information

2012 ◽  
Author(s):  
A. Sagheer ◽  
S. Aly
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Algorithm ◽  
Color Information

Research on Fast Face Detection Algorithm in Complex Background

2014 ◽  
Vol 1030-1032 ◽  
pp. 1779-1782
Author(s):  
Xin Wang ◽  
He Pan
Keyword(s):  
Prior Knowledge ◽  
Face Detection ◽  
Fast Algorithm ◽  
Vertical Direction ◽  
Detection Algorithm ◽  
Color Information ◽  
Human Eye ◽  
Face Region ◽  
Complex Background ◽  
The Face

This paper presents a fast algorithm for face detection in complex background, in which image color information is used first, project upper part of the partition of face in the gray image to horizontal and vertical direction. Determine the eyes positions by the minimum value, and scope the human eye in the face of prior knowledge to judge and adjust the face region.

A LabVIEW Design for Frontal and Non-Frontal Human Face Detection System in Complex Background

2014 ◽  
Vol 490-491 ◽  
pp. 1259-1266 ◽  
Author(s):  
Muralindran Mariappan ◽  
Manimehala Nadarajan ◽  
Rosalyn R. Porle ◽  
Vigneswaran Ramu ◽  
Brendan Khoo Teng Thiam
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Detection System ◽  
Detection Algorithm ◽  
Biometric Technology ◽  
Main Challenge ◽  
Human Face Detection ◽  
Color Detection ◽  
Large Face

Biometric identification has advanced vastly since many decades ago. It became a blooming area for research as biometric technology has been used extensively in areas like robotics, surveillance, security and others. Face technology is more preferable due to its reliability and accuracy. By and large, face detection is the first processing stage that is performed before extending to face identification or tracking. The main challenge in face detection is the sensitiveness of the detection to pose, illumination, background and orientation. Thus, it is crucial to design a face detection system that can accommodate those problems. In this paper, a face detection algorithm is developed and designed in LabVIEW that is flexible to adapt changes in background and different face angle. Skin color detection method blending with edge and circle detection is used to improve the accuracy of face detected. The overall system designed in LabVIEW was tested in real time and it achieves accuracy about 97%.

A Face Detection Algorithm in Color Image Based on Skin Color Segmentation

2013 ◽  
Vol 811 ◽  
pp. 417-421
Author(s):  
Shi Lei
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Image ◽  
Detection Algorithm ◽  
Color Segmentation ◽  
Skin Region ◽  
Otsu Method ◽  
The Face ◽  
Segmentation Image ◽  
Ellipse Area

Aiming at color images under complex background, this paper put forward a face detection algorithm based on skin color segmentation, combining the geometric characteristics. The skin region can be obtained by using skin color model and OTSU method to automatically optimize threshold segmentation image. By analyzing the characteristics of skin color region, the face position is determined by criterion of ellipse area.

Research of Video Surveillance System Base on Face Recognition and Motion Detection

2011 ◽  
Vol 271-273 ◽  
pp. 961-966
Author(s):  
Da Guang Jiang ◽  
Jun Kai Yi ◽  
Gao Hui Bian
Keyword(s):  
Face Detection ◽  
Motion Detection ◽  
Surveillance System ◽  
Skin Color ◽  
Moving Objects ◽  
Image Sequence ◽  
Detection Algorithm ◽  
Improved Method ◽  
Face Area ◽  
Slow Moving

In this paper, by using skin-color feature, especial location and pixel features of eyes in face area, an efficient face detection algorithm was designed. After face detection, discrete cosine Transform (DCT) was used to extract a set of observation, which is provided to train and recognize faces in the way of Hidden Markov Model (HMM). In order to solve the shortcoming that traditional motion detection algorithm can not be used to detect slow moving objects from an image sequence, an improved method was proposed by rebuilding the background.

Research and Improvement on the Algorithm of Face Region Detection Based on Skin Color Model

2014 ◽  
Vol 543-547 ◽  
pp. 2702-2705
Author(s):  
Hong Hai Liu ◽  
Xiang Hua Hou
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Algorithm ◽  
Face Image ◽  
Color Model ◽  
Region Detection ◽  
Face Region ◽  
The Face ◽  
Skin Color Model ◽  
Better Than

In face image with complex background, the CbCr skin color region will have offset when considering the illumination change. Therefore, the non-skin color pixels which luminance is less than 80 will be mistaken as skin color pixels and the skin color pixels which luminance is greater than 230 will be mistaken as non-skin color pixels. In order to reduce the misjudgments, an improved skin color model of nonlinear piecewise is put forward in this paper. Firstly, the skin color model of non-piecewise is analyzed and the experimental results show that by this model there is an obvious misjudgment in face detection. Then the skin color model of nonlinear piecewise is mainly analyzed and is demonstrated by mathematics method. A large number of training results show that the skin color model of nonlinear piecewise has better clustering distribution than the skin color model of non-piecewise. At lastly, the face detection algorithm adopting skin color model of nonlinear piecewise is analyzed. The results show that this algorithm is better than the algorithm adopting skin color model of non-piecewise and it makes a good foundation for the application of face image.

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