Research on Non-frontal Face Detection Method Based on Skin Color and Region Segmentation

2017 ◽  
pp. 46-52
Author(s):  
Haonan Wang ◽  
Tianfei Shen
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Region Segmentation

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%.

scholarly journals A Face Detection Method Based on Skin Color Model and Improved AdaBoost Algorithm

2020 ◽  
Vol 37 (6) ◽  
pp. 929-937
Author(s):  
Xiaoying Yang ◽  
Nannan Liang ◽  
Wei Zhou ◽  
Hongmei Lu
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Rate ◽  
Detection Method ◽  
Random Search ◽  
Color Space ◽  
Color Model ◽  
High Detection Rate ◽  
Adaboost Algorithm ◽  
Skin Color Model

This paper integrates skin color model and improved AdaBoost into a face detection method for high-resolution images with complex backgrounds. Firstly, the skin color areas were detected in a multi-color space. Each image was subject to adaptive brightness compensation, and converted into the YCbCr space, and a skin color model was established to solve face similarity. After eliminating the background interference by morphological method, the skin color areas were segmented to obtain the candidate face areas. Next, the inertia weight control factors and random search factor were introduced to optimize the global search ability of particle swarm optimization (PSO). The improved PSO was adopted to optimize the initial connection weights and output thresholds of the neural network. After that, a strong AdaBoost classifier was designed based on optimized weak BPNN classifiers, and the weight distribution strategy of AdaBoost was further improved. Finally, the improved AdaBoost was employed to detect the final face areas among the candidate areas. Simulation results show that our face detection method achieved high detection rate at a fast speed, and lowered false detection rate and missed detection rate.

Face Detection Based on Skin and Improved ICA

2011 ◽  
Vol 219-220 ◽  
pp. 1486-1490
Author(s):  
Yan Wang ◽  
Zheng Wu Jiang
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Color Space ◽  
Research Direction ◽  
Color Images ◽  
Independent Research ◽  
Image Preprocessing ◽  
In The Beginning ◽  
Speed And Accuracy

Face detection has been developed into one independent research direction. This paper proposes a detection method based on the combination of skin color and improved ICA. The method makes face detection in color images real and abandons the conventional way of detecting grey rather than color images. Its description is as follows: In the beginning, skin color mode should be founded on the basis of a new color space called H_SI_I. Then with the help of prior knowledge, image preprocessing can be implemented to obtain candidates’ faces, which will be detected by improved ICA. Experiments demonstrate that the method proposed by this paper has better effect than the conventional face detection based on subspace as far as speed and accuracy is concerned.

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