Difference in Lights and Color Background Differentiates the Color Skin Model in Face Detection for Security Surveillance

2017 ◽  
pp. 127-135 ◽  
Author(s):  
Dimple Chawla ◽  
Munesh Chandra Trivedi
Keyword(s):  
Face Detection ◽  
Skin Model ◽  
Color Background ◽  
Security Surveillance

scholarly journals Raspberry Pi based Security Surveillance with Face Detection

IARJSET ◽  
2017 ◽  
Vol 4 (4) ◽  
pp. 9-11
Author(s):  
Ms. Patil Sahyadri N ◽  
Mr. Devrukhkar Akshay S
Keyword(s):  
Face Detection ◽  
Raspberry Pi ◽  
Security Surveillance

The research of face detection based on double skin model and Adaboost algorithm

2013 ◽  
Author(s):  
Jianfeng Zhang ◽  
Jun Li ◽  
Shuming Jiang ◽  
Zhiqiang Wei ◽  
Jian Li ◽  
...  
Keyword(s):  
Face Detection ◽  
Skin Model ◽  
Adaboost Algorithm ◽  
Double Skin

Face Detection Based on Statistical Color Model and Haar Classifier

2012 ◽  
Vol 532-533 ◽  
pp. 634-638
Author(s):  
Xi Bin Jia ◽  
Lu Yi Li
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Speech Synthesis ◽  
Detection Algorithm ◽  
Visual Speech ◽  
Training Dataset ◽  
Color Model ◽  
Skin Area ◽  
Skin Model ◽  
The Face

The paper realizes the face detection algorithm based on the combination of the skin model and the Haar algorithm. Firstly, a platform for sample labeling was constructed, which combines the contour extraction algorithm with manual labeling. By labeling more than 10000 images obtained randomly from the Internet, a large training dataset is available. Then, a skin histogram, a non-skin histogram and a statistical skin model are constructed by analyzing the distribution of the skin and the non-skin color on the basis of a large training dataset. Based on this statistical color model, the skin area is detected and split from video files frame by frame. With the Haar Object Detection algorithm and the morphology algorithm such as erosion and dilation, the background noise and non-face areas are removed from the detected skin area and facial area is detected, which provides the basis for face recognition and the video-based visual speech synthesis. Compared with the Haar-based face detection method, our algorithm greatly improves the rate of correct detection and reduces the rate of the false positives.

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