Automatic skin-color distribution extraction for face detection and tracking

2002 ◽  
Author(s):  
S. Kawato ◽  
J. Ohya
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Distribution ◽  
Detection And Tracking

scholarly journals A NOVEL FACE DETECTION AND TRACKING ALGORITHM IN REAL- TIME VIDEO SEQUENCES

2013 ◽  
pp. 25-29
Author(s):  
NAGAPRIYA KAMATH K ◽  
ASHWINI HOLLA ◽  
SUBRAMANYA BHAT
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Template Matching ◽  
Digital Images ◽  
Color Space ◽  
Color Model ◽  
Hsv Color Space ◽  
Detection And Tracking ◽  
Skin Color Model ◽  
Human Faces

Face detection is a image processing technology that determines the location and size of human faces in digital images or video. This module precedes face recognition systems that plays an important role in applications such as video surveillance, human computer interaction and so on. This proposed work focuses mainly on multiple face detection technique, taking into account the variations in digital images or video such as face pose, appearances and illumination. The work is based on skin color model in YCbCr and HSV color space. First stage of this proposed method is to develop a skin color model and then applying the skin color segmentation in order to specify all skin regions in an image. Secondly, a template matching is done to assure that the segmented image does not contain any non-facial part. This algorithm works to be robust and efficient.

An Enhanced Approach of Face Detection using Bacteria Foraging Technique

2016 ◽  
Vol 6 (1) ◽  
pp. 1-11
Author(s):  
Kapil Kumar Gupta ◽  
Rizwan Beg ◽  
Jitendra Kumar Niranjan
Keyword(s):  
Human Skin ◽  
Face Detection ◽  
Skin Color ◽  
Detection Rate ◽  
Processing Time ◽  
Detection Algorithm ◽  
Human Face ◽  
Local Color ◽  
Color Distribution ◽  
Number Of Bacteria

In this study, authors present an enhanced approach of face detection using bacteria foraging technique. This approach is based on chemotexis, reproduction and elimination and dispersal step. In this study the authors analysed face detection algorithm based on human skin color and fitting the ellipse as human face can be approximate by ellipse. Their approach for face detection requires no initial pre-processing of the image. A number of Bacteria agents with evolutionary behaviours are uniformly distributed in the 2-D image environment to search the skin-like pixels and locate each face-like region by evaluating the local color distribution. This approach has the advantage of very fast face detection by reducing pre-processing time of the image. This approach significantly improves face detection rate.

Human Face Detection and Tracking using Skin Color Modeling and Connected Component Operators

2002 ◽  
Vol 48 (3-4) ◽  
pp. 289-293 ◽  
Author(s):  
Prem Kuchi ◽  
Prasad Gabbur ◽  
P Subbanna Bhat ◽  
S Sumam David
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Human Face ◽  
Connected Component ◽  
Detection And Tracking ◽  
Human Face Detection

REGION-BASED AND CONTENT ADAPTIVE SKIN DETECTION IN COLOR IMAGES

2007 ◽  
Vol 21 (05) ◽  
pp. 831-853 ◽  
Author(s):  
WEI-CHE CHEN ◽  
MING-SHI WANG
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Gaussian Mixture ◽  
Performance Comparison ◽  
Skin Detection ◽  
Detection Techniques ◽  
Detection And Tracking ◽  
Gaussian Classifier ◽  
Special Region ◽  
Made In

Skin detection plays an important role in applications such as face detection and tracking, person detection and pornography detection. While previous studies focus on pixel-based skin color detection techniques that individually classify each pixel as skin color or non-skin color, this study presents a region-based algorithm for detecting skin color. The proposed algorithm uses a special region, called key skin region, as the basis to classify skin color. A performance comparison with conventional skin classifiers, including the Bayesian classifier, the unimodal Gaussian classifier and the Gaussian mixture classifier, is made in this study. Experimental results show that the proposed algorithm outperforms other tested skin classifiers. Furthermore, the skin regions detected by the proposed algorithm, especially facial regions, are nearly complete with no hollow holes in these regions. This property can simplify the complexity of implementing applications that use skin color as their basis, such as face detection and face tracking.

Face detection in single and multiple images using different skin color models

2020 ◽  
Vol 13 ◽  
Author(s):  
Manpreet Kaur ◽  
Jasdev Bhatti ◽  
Mohit Kumar Kakkar ◽  
Arun Upmanyu
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Input Image ◽  
Morphological Operations ◽  
Reliability Model ◽  
Multiple Images ◽  
Face Region ◽  
Bounding Box ◽  
The Face ◽  
Precision And Accuracy

Introduction: Face Detection is used in many different steams like video conferencing, human-computer interface, in face detection, and in the database management of image. Therefore, the aim of our paper is to apply Red Green Blue ( Methods: The morphological operations are performed in the face region to a number of pixels as the proposed parameter to check either an input image contains face region or not. Canny edge detection is also used to show the boundaries of a candidate face region, in the end, the face can be shown detected by using bounding box around the face. Results: The reliability model has also been proposed for detecting the faces in single and multiple images. The results of the experiments reflect that the algorithm been proposed performs very well in each model for detecting the faces in single and multiple images and the reliability model provides the best fit by analyzing the precision and accuracy. Moreover Discussion: The calculated results show that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images. Also, the evaluated results by this paper provides the better testing strategies that helps to develop new techniques which leads to an increase in research effectiveness. Conclusion: The calculated value of all parameters is helpful for proving that the proposed algorithm has been performed very well in each model for detecting the face by using a bounding box around the face in single as well as multiple images. The precision and accuracy of all three models are analyzed through the reliability model. The comparison calculated in this paper reflects that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images.

Sign in / Sign up

Export Citation Format

Share Document