scholarly journals Human Face Detection by Using Skin Color Segmentation, Face Features and Regions Properties

2012 ◽  
Vol 38 (9) ◽  
pp. 14-17 ◽  
Author(s):  
Devendra SinghRaghuvanshi ◽  
Dheeraj Agrawal
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Segmentation ◽  
Human Face ◽  
Face Features ◽  
Human Face Detection

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.

The left-behind human detection and tracking system based on vision with multi-model fusion and microwave radar inside the bus

2020 ◽  
Vol 234 (9) ◽  
pp. 2342-2354
Author(s):  
Jiacai Liao ◽  
Guoliang Xiang ◽  
Libo Cao ◽  
JiaHao Xia ◽  
Luyao Yue
Keyword(s):  
Face Detection ◽  
Human Detection ◽  
Detection Accuracy ◽  
Human Face ◽  
Detection Model ◽  
Left Behind ◽  
Microwave Radar ◽  
Face Features ◽  
Human Face Detection ◽  
Radar Information

Left-behind humans inside the car or bus have caused a lot of accidents, so it is essential to detect the humans in vehicle. Current human detection methods rely on wearable devices, oxygen sensors, and special seat designs in vehicles, but those sensors cannot adapt to ever-changing environments. To solve those problems and especially to improve passengers’ safety on the bus, we propose a method to accomplishing human detection by fusion vision and microwave radar information in various environments in vehicle. For vision information, we use different networks to extract human and human face features, and fusion of the detection results in different models to improve human detection accuracy. The human detection model is MobileNet-V2, and the human face detection model is MTCNN. A new matching schedule and tracking objects management rule based on the Kernelized Correlation Filter tracker are designed to track the human and human face detection boxes. The microwave radar information is used to detect moving objects. Finally, the fusion vision and microwave radar detection results are implemented. Experiments show that our method has improved the human detection accuracy in vehicle, and this method can be used for detection of left-behind children on the school bus.

scholarly journals A New Algorithm for Human Face Detection Using Skin Color Tone

2013 ◽  
Vol 11 (6) ◽  
pp. 31-38 ◽  
Author(s):  
Hewa Majeed Zangana Hewa Majeed Zangana
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Human Face ◽  
Color Tone ◽  
Human Face Detection
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