scholarly journals An Innovative Face Detection Based on YCgCr Color Space

2018 ◽  
Author(s):  
Solly Aryza
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Image ◽  
Color Space ◽  
Gaussian Model ◽  
Detection Rates ◽  
Detection Algorithms ◽  
Lighting Conditions ◽  
Wide Range ◽  
Human Faces

It is very challenging to recognize a face from an image due to the wide variety of face and the uncertain of face position. The research on detecting human faces in color image and in video sequence has been attracted with more and more people. In this paper, we propose a novel face detection method that achieves better detection rates. The new face detection algorithms based on skin color model in YCgCr chrominance space. Firstly, we build a skin Gaussian model in Cg-Cr color space. Secondly, a calculation of correlation coefficient is performed between the given template and the candidates. Experimental results demonstrate that our system has achieved high detection rates and low false positives over a wide range of facial variations in color, position and varying lighting conditions.

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.

Comparative Analysis Of Efficient Image Segmentation Technique For Text Recognition And Human Skin Recognition

2021 ◽  
Vol 1 (1) ◽  
pp. 81-90
Author(s):  
Septian Cahyadi ◽  
Febri Damatraseta ◽  
Lodryck Lodefikus S
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Convolutional Neural Network ◽  
Human Skin ◽  
Skin Color ◽  
Color Space ◽  
Text Recognition ◽  
Lighting Conditions ◽  
Wide Range ◽  
Computer Vision Technology

Computer Vision and Pattern Recognition is one of the most interesting research subject on computer science, especially in case of reading or recognition of objects in realtime from the camera device. Object detection has wide range of segments, in this study we will try to find where the better methodologies for detecting a text and human skin. This study aims to develop a computer vision technology that will be used to help people with disabilities, especially illiterate (tuna aksara) and deaf (penyandang tuli) to recognize and learn the letters of the alphabet (A-Z). Based on our research, it is found that the best method and technique used for text recognition is Convolutional Neural Network with achievement accuracy reaches 93%, the next best achievement obtained OCR method, which reached 98% on the reading plate number. And also OCR method are 88% with stable image reading and good lighting conditions as well as the standard font type of a book. Meanwhile, best method and technique to detect human skin is by using Skin Color Segmentation: CIELab color space with accuracy of 96.87%. While the algorithm for classification using Convolutional Neural Network (CNN), the accuracy rate of 98% Key word: Computer Vision, Segmentation, Object Recognition, Text Recognition, Skin Color Detection, Motion Detection, Disability Application

scholarly journals An ICSC Model for Detecting Human Skin in JPEG Images

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Human Skin ◽  
Face Detection ◽  
Skin Color ◽  
Gaussian Model ◽  
Color Model ◽  
Threshold Method ◽  
Detection Rates ◽  
Jpeg Images ◽  
Skin Color Model ◽  
Localization And Tracking

The detection of human skin color has proven to be a useful and robust technique for detecting nude images, face detection, localization and tracking. This paper presents an Improved Chromatic Skin Color model to detect the human skin in JPEG images; the ICSC model detected the human skin with detection rates more than 90%. A threshold method and 2D Gaussian model will improve the accuracy of skin regions detected

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.

scholarly journals ADAPTIVE SKIN DETECTION UNDER UNCONSTRAINED LIGHTING CONDITIONS USING A BIGAUSSIAN MODEL AND ILLUMINATION ESTIMATION

2011 ◽  
Vol 24 (1) ◽  
pp. 21 ◽  
Author(s):  
Jian-Hua Zheng ◽  
Chong-Yang Hao ◽  
Yang-Yu Fan ◽  
Xian-Yong Zang
Keyword(s):  
Skin Color ◽  
Gaussian Model ◽  
Classification Performance ◽  
Skin Detection ◽  
Illumination Estimation ◽  
Skin Segmentation ◽  
Detection Model ◽  
Detection Algorithms ◽  
Lighting Conditions ◽  
Combined Models

An algorithm is proposed to improve the performance of skin detection algorithms under poor illumination conditions. A hybrid skin detection model is addressed to solve these problems by combining two Gaussian models of skin under normal conditions and bright illumination. According to the distribution of the combined models, the algorithm automatically evaluates the skin segmentation result of an adaptive threshold algorithm based on a Gaussian model by estimating the illumination conditions of image. If the estimation result shows that the illumination condition is very different from the normal one, the skin color of the original image needs compensation, and then the algorithm feeds the compensated image back to the Gaussian model for finer skin detection. The experimental results show that our algorithm can cope with a complex illumination change and greatly improve skin classification performance under inferior illumination conditions.

Face Detection Method Using PCNN and Skin Color Model

2012 ◽  
Vol 562-564 ◽  
pp. 1377-1381
Author(s):  
Dong Ming Zhou ◽  
Hong Cai
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Color Image ◽  
Ignition Time ◽  
Color Space ◽  
Target Face ◽  
Color Model ◽  
Skin Area ◽  
Skin Color Model

This paper presented a face detection method for the color image using pulse coupled neural network (PCNN) and skin color model. The color image which is processed well through light compensation is converted from RGB to YCbCr color space, then the skin area are divided into sub-block, and skin color segmentation is made for the image in YCbCr space. Finally, we use PCNN to extract all sub-block ignition time sequence, and calculate various sub-block difference degrees between target face and the tested image, if the difference degree is the smallest, then the target face himself is the same person. Experimental results show that the proposed method has higher accuracy and robustness, can obtain satisfactory detection effect.

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 Skin color variation: A study on Eastern and North East India

2019 ◽  
Vol 10 (3) ◽  
pp. 13-16
Author(s):  
Sumit Maitra ◽  
Diptendu Chatterjee ◽  
Arup Ratan Bandyopadhyay
Keyword(s):  
Ethnic Groups ◽  
Skin Color ◽  
Skin Pigmentation ◽  
Color Space ◽  
Color Variation ◽  
Phenotypic Traits ◽  
North East India ◽  
North East ◽  
Wide Range ◽  
Significant Difference

Background: Skin pigmentation is one of the most variable phenotypic traits and most noticeable of human polymorphisms. Skin pigmentation in humans is largely determined by the quantity and distribution of the pigment melanin. The literature review on skin color variation revealed a few works on skin pigmentation variation has been conducted in India from Southern, Western and Northern part. Aims and Objectives: To best of the knowledge, the present discourse is the first attempt to understand skin color variation from Eastern and North Eastern part of India among three populations. Materials and Methods: The present study consisted of 312 participants from Chakma and Tripuri groups of Tripura, North East India, and participants from Bengalee Hindu caste population from West Bengal. Skin color was measured by Konica Minolta CR-10 spectrophotometer which measures and quantifies the colors with a 3D color space (CIELAB) color space created by 3 axes. All the skin color measurements from each participant were taken from unexposed (underarm) left and right to get a mean and exposed (forehead) to sunlight. Results: The distribution of skin color variation among the three populations demonstrated significant (p<0.05) difference in lightness for unexposed and exposed indicating lightness in unexposed area. Furthermore, the present study revealed significant difference (p<0.05) in skin color among the ethnic groups across the body location and all three attributes (lightness, redness and yellowness). Conclusion: Generally, skin color variation may be elucidated by two main factors: individual differences in lightness and yellowness and by and large due to ethnicity, where diversity in redness is due to primarily due to different body locations. Variation in lightness have more characteristic probability. The present study first time reports the wide range of quantitative skin color variation among the three ethnic groups from Eastern and North East India and highest yellowness (b*) among the population from North East India.

An Improved Otsu’s Thresholding Algorithm on Gesture Segmentation

2017 ◽  
Vol 21 (2) ◽  
pp. 247-250 ◽  
Author(s):  
Chongshan Lv ◽  
◽  
Ting Zhang ◽  
Chengyuan Liu
Keyword(s):  
Gesture Recognition ◽  
Skin Color ◽  
Color Space ◽  
Gaussian Model ◽  
Adaptive Threshold ◽  
Ycbcr Color Space ◽  
Gesture Segmentation ◽  
Rgb Color Space ◽  
Similarity Graph ◽  
Thresholding Algorithm

In gesture recognition systems, segmenting gestures from complex background is the hardest and the most critical part. Gesture segmentation is the prerequisite of following image processing, and the result of segmentation has a direct influence on the result of gesture recognition. This paper proposed an algorithm of adaptive threshold gesture segmentation based on skin color. First of all, the image should be transformed from RGB color space to YCbCr color space. After eliminating luminance component Y, similarity graph of skin color will be obtained from the Gaussian model. Then Otsu adaptive threshold algorithm is used to carry out binary processing for the similarity graph of skin color. After the segmentation of skin color regions, the morphology method is used to process binary image for determining the location of hands. Experimental results show that the detailed segmentation of skin color using the dynamic-adaptive threshold can improve noise resistance and can produce better results.

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