scholarly journals Using Skin Color and HAD-AdaBoost Algorithm for Face Detection in Color Images

2012 ◽  
Author(s):  
Li-Jie Xue ◽  
Zheng-Ming Li
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Images ◽  
Adaboost Algorithm

scholarly journals Human Face Detection in Color Images

2021 ◽  
Author(s):  
Jun Gao
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Video Retrieval ◽  
Color Images ◽  
Human Face ◽  
Detection Scheme ◽  
Retrieval Systems ◽  
Human Face Detection ◽  
The Face ◽  
Video Retrieval Systems

Detection of human face has many realistic and important applications such as human and computer interface, face recognition, face image database management, security access control systems and content-based indexing video retrieval systems. In this report a face detection scheme will be presented. The scheme is designed to operate on color images. In the first stage of algorithm, the skin color regions are detected based on the chrominance information. A color segmentation stage is then employed to make skin color regions to be divided into smaller regions which have homogenous color. Then, we use the iterative luminance segmentation to further separate the detected skin region from other skin-colored objects such as hair, clothes, and wood, based on the high variance of the luminance component in the neighborhood of edges of objects. Post-processing is applied to determine whether skin color regions fit the face constrains on density of skin, size, shape and symmetry and contain the facial features such as eyes and mouths. Experimental results show that the algorithm is robust and is capable of detecting multiple faces in the presence of a complex background which contains the color similar to the skin tone.

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.

Multi-View Face Detection Based on Multi-Features AdaBoost Collaborative Learning Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 884-888
Author(s):  
Rong Bing Huang ◽  
Hong Zhang ◽  
Chang Ming Shu
Keyword(s):  
Collaborative Learning ◽  
Face Detection ◽  
Skin Color ◽  
Local Binary Pattern ◽  
Learning Algorithm ◽  
Standard Test ◽  
System Structure ◽  
Adaboost Algorithm ◽  
The Face ◽  
Candidate Regions

In View of the Multi-View Face Detection Problem under Complex Background, an Improved Face Detection Method Based on Multi-Features Boosting Collaborative Learning Algorithm Integrating Local Binary Pattern (LBP) is Presented. Firstly, Facial Skin Color Information is Used to Exclude most of the Background Regions. then, Haar-like Feature and LBP Feature are Extracted from Facial Candidate Regions and Inputted into a Modified Adaboost Algorithm to Obtain a Strong Classifier. Lastly, in Order to Improve the Detection Speed, Pyramid Classifier System Structure is Adopted to Determine the Face. the Experimental Results on CMU Standard Test Set and Life Photos, the Proposed Method has Achieved the Rapid Detection of Multi-View Face Image.

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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