scholarly journals Skin Detection and Segmentation of Human Face in Color Images

2011 ◽  
Vol 4 (1) ◽  
pp. 10-17 ◽  
Author(s):  
Baozhu Wang ◽  
◽  
Xiuying Chang ◽  
Cuixiang Liu ◽  
◽  
...  
Keyword(s):  
Color Images ◽  
Skin Detection ◽  
Human Face

Human Skin Detection in Color Images Using Deep Learning

2015 ◽  
Vol 5 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Mohammadreza Hajiarbabi ◽  
Arvin Agah
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Human Skin ◽  
Skin Color ◽  
Color Image ◽  
Gaussian Model ◽  
Color Images ◽  
Skin Detection ◽  
Learning Methods ◽  
Rule Based

Human skin detection is an important and challenging problem in computer vision. Skin detection can be used as the first phase in face detection when using color images. The differences in illumination and ranges of skin colors have made skin detection a challenging task. Gaussian model, rule based methods, and artificial neural networks are methods that have been used for human skin color detection. Deep learning methods are new techniques in learning that have shown improved classification power compared to neural networks. In this paper the authors use deep learning methods in order to enhance the capabilities of skin detection algorithms. Several experiments have been performed using auto encoders and different color spaces. The proposed technique is evaluated compare with other available methods in this domain using two color image databases. The results show that skin detection utilizing deep learning has better results compared to other methods such as rule-based, Gaussian model and feed forward neural network.

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.

Color Images Illumination Compensation Method for Human Face Region Detection

2014 ◽  
Vol 678 ◽  
pp. 162-165 ◽  
Author(s):  
Yang Yu ◽  
Xiao Bin Li ◽  
Hai Yan Sun
Keyword(s):  
Color Images ◽  
Compensation Method ◽  
Detection Accuracy ◽  
Human Face ◽  
Light Conditions ◽  
Illumination Compensation ◽  
Facial Region ◽  
Region Detection ◽  
Face Region ◽  
California Institute Of Technology

Facial region detection has broad application prospects, but existing human face region detection methods have some rigorous requirements for the light conditions. Error detection about face area caused by the poor light conditions has a great bad effect on the follow-up processing, such as face recognition, fatigue degree evaluation based on visual. So face region detection in complex lighting conditions has always been the difficult problem. Therefore a self-adaptive illumination compensation method for color images has been proposed. Select the color face images database of the California Institute of Technology to test the method dealing with the face region detection by original image and the image after illumination compensation. In the simulation experiment, the method of Illumination compensation can effectively improve the detection accuracy. Lay the foundation for driver fatigue detection based on visual.

HUMAN FACE DETECTION IN COLOR IMAGES

2004 ◽  
Vol 07 (03n04) ◽  
pp. 369-383 ◽  
Author(s):  
IHAB ZAQOUT ◽  
ROZIATI ZAINUDDIN ◽  
SAPIAN BABA
Keyword(s):  
Face Detection ◽  
Color Images ◽  
Human Face ◽  
Skin Segmentation ◽  
Elliptical Shape ◽  
Fitting Algorithm ◽  
Human Face Detection ◽  
Lookup Tables ◽  
The Face ◽  
The Relationship

In this paper we have used a simple and efficient color-based approach to segment human skin pixels from background, using a 2D histogram-based approach as a preprocess stage for human face detection. For skin segmentation, a total of 446,007 skin samples from the training set is manually cropped from the RGB color images, to calculate three lookup tables based on the relationship between each pair of the triple components (R, G, B). Derivation of skin classifier rules from the lookup tables are based on how often each attribute value (interval) occurs, and their associated certainty values. For face detection, we assume the face-appearance as blob-like, and that the face has an approximately elliptical shape. Accordingly, an ellipse-fitting algorithm is appropriate, which is based on statistical moments, and those blobs that have an elliptical shape are retained as face candidates.

scholarly journals An Automatic Face Detection System for RGB Images

2011 ◽  
Vol 6 (1) ◽  
pp. 21 ◽  
Author(s):  
Tudor Barbu
Keyword(s):  
Face Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Detection System ◽  
Color Images ◽  
Human Face ◽  
Skin Region ◽  
Detection Approach ◽  
Rgb Images ◽  
Digital Color Images

We propose a robust face detection approach that works for digital color images. Our automatic detection method is based on image skin regions, therefore a skin-based segmentation of RGB images is provided first. Then, we decide for each skin region if it represents a human face or not, using a set of candidate criteria, an edge detection process, a correlation based technique and a threshold-based method. A high face detection rate is obtained using the proposed method.

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