Facial features detection in color images based on skin color segmentation

2014 ◽  
Author(s):  
Md. Mehedi Hasan ◽  
Md. Foisal Hossain
Keyword(s):  
Skin Color ◽  
Color Images ◽  
Color Segmentation ◽  
Facial Features

DETECTION OF MULTIPLE FACES IN AN IMAGE USING SKIN COLOR INFORMATION AND LINES-OF-SEPARABILITY FACE MODEL

2006 ◽  
Vol 20 (01) ◽  
pp. 39-61 ◽  
Author(s):  
P. S. HIREMATH ◽  
AJIT DANTI
Keyword(s):  
Skin Color ◽  
Geometric Approach ◽  
Color Segmentation ◽  
Color Model ◽  
Facial Features ◽  
Color Information ◽  
Face Model ◽  
The Face ◽  
Human Faces ◽  
Control Limits

In this paper, human faces are detected using the skin color information and the Lines-of-Separability (LS) face model. The various skin color spaces based on widely used color models such as RGB, HSV, YCbCr, YUV and YIQ are compared and an appropriate color model is selected for the purpose of skin color segmentation. The proposed approach of skin color segmentation is based on YCbCr color model and sigma control limits for variations in its color components. The segmentation by the proposed method is found to be more efficient in terms of speed and accuracy. Each of the skin segmented regions is then searched for the facial features using the LS face model to detect the face present in it. The LS face model is a geometric approach in which the spatial relationships among the facial features are determined for the purpose of face detection. Hence, the proposed approach based on the combination of skin color segmentation and LS face model is able to detect single as well as multiple faces present in a given image. The experimental results and comparative analysis demonstrate the effectiveness of this approach.

scholarly journals FACE DETECTION ON GRAYSCALE AND COLOR IMAGES USING COMBINED CASCADE OF CLASSIFIERS

2014 ◽  
pp. 61-71
Author(s):  
Yuriy Kurylyak ◽  
Ihor Paliy ◽  
Anatoly Sachenko ◽  
Amine Chohra ◽  
Kurosh Madani
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection System ◽  
Color Space ◽  
Color Images ◽  
Detection Methods ◽  
Color Segmentation ◽  
Detection Rates ◽  
Verification Method ◽  
High Processing Speed

The paper describes improved face detection methods for grayscale and color images using the combined cascade of classifiers and skin color segmentation. The combined cascade with proposed face candidates’ verification method allows achieving one of the best detection rates on CMU test set and a high processing speed suitable for a video flow processing. It’s also shown that the mixture of color spaces is more efficient during the skin color segmentation than the application of one color space. A lot of experiments are made to choose rational parameters for the developed face detection system in order to improve the detection rate, false positives’ number and system’s speed.

EXTRACTING FACES AND FACIAL FEATURES FROM COLOR IMAGES

2008 ◽  
Vol 22 (03) ◽  
pp. 515-534 ◽  
Author(s):  
FRANK Y. SHIH ◽  
SHOUXIAN CHENG ◽  
CHAO-FA CHUANG ◽  
PATRICK S. P. WANG
Keyword(s):  
Skin Color ◽  
Color Image ◽  
Facial Feature ◽  
Noise Removal ◽  
Color Images ◽  
Support Vector ◽  
Facial Features ◽  
Hole Filling ◽  
Knowledge Rules ◽  
Human Faces

In this paper, we present image processing and pattern recognition techniques to extract human faces and facial features from color images. First, we segment a color image into skin and non-skin regions by a Gaussian skin-color model. Then, we apply mathematical morphology and region filling techniques for noise removal and hole filling. We determine whether a skin region is a face candidate by its size and shape. Principle component analysis (PCA) is used to verify face candidates. We create an ellipse model to locate eyes and mouths areas roughly, and apply the support vector machine (SVM) to classify them. Finally, we develop knowledge rules to verify eyes. Experimental results show that our algorithm achieves the accuracy rate of 96.7% in face detection and 90.0% in facial feature extraction.

A Novel Approach of Face Detection Based on Skin Color Segmentation and SVM

2011 ◽  
Vol 225-226 ◽  
pp. 437-441
Author(s):  
Jing Zhang ◽  
You Li
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Information Access ◽  
Color Segmentation ◽  
Support Vector ◽  
Svm Classifier ◽  
Novel Approach ◽  
Great Performance ◽  
Face Detection And Recognition ◽  
Detection And Recognition

Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on skin color segmentation and Support Vector Machine(SVM) is proposed. Firstly, segmenting image using color model to filter candidate faces roughly; And then Eye-analogue segments at a given scale are discovered by finding regions which are darker than their neighborhoods to filter candidate faces farther; at the end, SVM classifier is used to detect face feature in the test image, SVM has great performance in classification task. Our tests in this paper are based on MIT face database. The experimental results demonstrate that the proposed method is encouraging with a successful detection rate.

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.

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