An Eye Localization Method Based on the Skin Color Feature and Otsu Algorithm

2013 ◽  
Vol 401-403 ◽  
pp. 1324-1327 ◽  
Author(s):  
Xiu Fang Yang ◽  
Yin Cheng Qi ◽  
Jie Yao
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Color Segmentation ◽  
Search Region ◽  
Color Feature ◽  
Integral Projection ◽  
Eye Localization ◽  
Segmented Image ◽  
Facial Area ◽  
Left And Right

In order to eliminate the influences of illumination and face-poses on eye localization, a feasible method is proposed based on the skin color feature and Otsu algorithm. Firstly, we detect the skin color in YCb'Cr' color space. Skin color segmentation principle is used to narrow the search region in human eye detection. Then we convert the segmented image to a binary image by Otsu algorithm and extract the eye region. Finally, the left and right eyes are positioned in the facial area with the binary integral projection. An analysis of the detections reveals that this algorithm has good robustness against changes of illumination and face-pose.

Human Body Parts Detection Using YCbCr Color Space

2013 ◽  
Vol 393 ◽  
pp. 556-560
Author(s):  
Nurul Fatiha Johan ◽  
Yasir Mohd Mustafah ◽  
Nahrul Khair Alang Md Rashid
Keyword(s):  
Human Body ◽  
Skin Color ◽  
Color Space ◽  
Detection Algorithm ◽  
Color Segmentation ◽  
Color Model ◽  
Body Parts ◽  
Color Spaces ◽  
Morphological Operation ◽  
Ycbcr Color Space

Skin color is proved to be very useful technique for human body parts detection. The detection of human body parts using skin color has gained so much attention by many researchers in various applications especially in person tracking, search and rescue. In this paper, we propose a method for detecting human body parts using YCbCr color spaces in color images. The image captured in RGB format will be transformed into YCbCr color space. This color model will be converted to binary image by using color thresholding which contains the candidate human body parts like face and hands. The detection algorithm uses skin color segmentation and morphological operation.

scholarly journals Skin Color Segmentation in RGB Color Space by Adaptive Network Based Fuzzy Inference System (ANFIS)

2015 ◽  
Vol 37 ◽  
pp. 264 ◽  
Author(s):  
Mohammad Hussein Fakhravari ◽  
Marzieh Dadvar
Keyword(s):  
Fuzzy Inference System ◽  
Skin Color ◽  
Fuzzy Inference ◽  
Color Space ◽  
Color Segmentation ◽  
Data Mapping ◽  
Adaptive Network ◽  
Inference System ◽  
Wide Range ◽  
Rgb Color Space

Skin color detection is a popular and useful technique because of the wide range of application in both human computer interactions and analyses based on diagnostic. Therefore, providing an appropriate method for pixel-like skin parts can solve many problems. The presented color segmentation algorithm works directly in RGB color space without having to convert the color space. Using Genfis3 function, we formed the Sugeno fuzzy network and clustered the data using fuzzy C-Mean (FCM) clustering rule and for each class and cluster we considered a Rule. In the next step, the output resulting from pseudo-polynomial data mapping is provided as the input to Adaptive Network Based Fuzzy Inference System (ANFIS).

scholarly journals A multi-color based features from facial images for automatic ethnicity identification model

2020 ◽  
Vol 18 (3) ◽  
pp. 1383
Author(s):  
Mohd Zamri Osman ◽  
Mohd Aizaini Maarof ◽  
Mohd Foad Rohani ◽  
Nilam Nur Amir Sjarif ◽  
Nor Saradatul Akmar Zulkifli
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Skin Color ◽  
Color Space ◽  
Experimental Result ◽  
Demographic Information ◽  
Feature Selection Technique ◽  
Color Feature ◽  
Biometric Analysis ◽  
Identification Model

<span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification technique using color-based feature mostly failed to determine the ethnicity classes accurately due to low properties of features in color-based. Thus, this paper purposely analyses the accuracy of the color-based ethnicity identification model from various color spaces. The proposed model involved several phases such as skin color feature extraction, feature selection, and classification. In the feature extraction process, a dynamic skin color detection is adapted to extract the skin color information from the face candidate. The multi-color feature was formed from the descriptive statistical model. Feature selection technique applied to reduce the feature space dimensionality. Finally, the proposed ethnicity identification was tested using several classification algorithms. From the experimental result, we achieved a better result in multi-color feature compared to individual color space model under Random Forest algorithm.</span>

The Research on Hand Gestures Segmentation Based on Skin Color Detection Combination with Dynamic Threshold

2013 ◽  
Vol 756-759 ◽  
pp. 1938-1942 ◽  
Author(s):  
Yi Ting Wang ◽  
Feng Jing Shao
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Color Segmentation ◽  
Dynamic Threshold ◽  
Background Color ◽  
Threshold Method ◽  
Hand Gestures ◽  
Skin Color Detection ◽  
Color Detection ◽  
Rgb Color Space

Aiming at the shortcomings of hand gestures segmentation based on fixed threshold and the problem of the interference of background color, this paper proposes a research on hand gestures segmentation based on skin color detection. The captured image is translated to YCgCr color space from RGB color space, and then skin color segmentation is done by using dynamic threshold method, so the hand gestures segmentation is completed. Finally segmentation results are verified by experiments and the method is summarized.

scholarly journals Color Space to Detect Skin Image: The Procedure and Implication

2017 ◽  
Vol 4 (2) ◽  
pp. 143-149 ◽  
Author(s):  
Sukmawati Nur Endah ◽  
Retno Kusumaningrum ◽  
Helmie Arif Wibawa
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Image Data ◽  
Skin Detection ◽  
Test Results ◽  
Color Spaces ◽  
Color Feature

Skin detection is one of the processes to detect the presence of pornographic elements in an image. The most suitable feature for skin detection is the color feature. To be able to represent the skin color properly, it is needed to be processed in the appropriate color space. This study examines some color spaces to determine the most appropriate color space in detecting skin color. The color spaces in this case are RGB, HSV, HSL, YIQ, YUV, YCbCr, YPbPr, YDbDr, CIE XYZ, CIE L*a*b*, CIE L*u* v*, and CIE L*ch. Based on the test results using 400 image data consisting of 200 skin images and 200 non-skin images, it is obtained that the most appropriate color space to detect the color is CIE L*u*v*.

scholarly journals Identifikasi Wajah Dengan Segmentasi Warna Kulit Menggunakan Metode Viola Jones

2019 ◽  
Vol 4 (1) ◽  
pp. 1-6
Author(s):  
Andi Asni b ◽  
Tamara Octa Dana
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Skin Color ◽  
Detection System ◽  
Color Segmentation ◽  
Face Images ◽  
Detection Of Objects ◽  
The Face ◽  
Left And Right ◽  
Normal Light

Abstract - Face detection (face detection) is one of the initial steps that is very important before the face recognition process (face recognition). Face detection is the detection of objects in the form of faces in which there are special features that represent the shape of faces in general. One method of face detection is the Viola Jones method. Viola Jones method is used to detect faces and skin color segmentation, test data processing using Matlab and capture on a Smartphone. The test is carried out at normal light intensity with a predetermined distance and face position. The results of this study indicate the level of accuracy of detection of face image variations in the position of face images facing forward (frontal), sideways left and right 45̊. But it has a weakness of this face detection system that is unable to determine faces in images that have faces that are not upright (tilted) or not frontal (facing sideways) at a 90̊ angle. Face position that is upright / not upright will determine the success of this face detection. The level of identification of the Viola Jones simulation was 100% with 4 images consisting of 3 boys and 1 girl.

Sign in / Sign up

Export Citation Format

Share Document