Skin Color Model Research Articles

Real-time hand area segmentation for hand gesture recognition in desktop environment

the process of separating the hand area from a complex background, knownas hand segmentation, is a prerequisite for any vision-based hand gesture recognitionsystem. In some applications, only a rough estimate of the hand area is needed, but in otherapplications an exact segmentation of the hand, if possible, is needed. In this paper, threemethods for extracting the hand from the background in real-time were tested. Thesemethods use off-line learning of skin color; these methods are Histogram Intersection,Color Histogram, and Skin Color Modeling and Adaptation. The three methods weretested using a video sequence of 100 frames using four different lighting conditions, with400 frames in total. The different lighting conditions are fluorescent light only, a mixture offluorescent and daylight, daylight without the sun light, and daylight with the sun light.These video streams were taken from the same person under the above different lightingconditions. A comparative study between the three methods was performed. The resultsshowed that hand segmentation using Skin Color Modeling and Adaptation withFluorescent light produced the best results among the three methods. The objective of ourresearch is to accurately recognize the hand gestures, and then use it in many differentapplications. The work in this paper is the first stage in our research. The contribution inthis paper is the comparative study between three different techniques that use skin-colormodeling under different lighting conditions.

An ICSC Model for Detecting Human Skin in JPEG Images

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

Mechanisms for Color Convergence in a Mimetic Radiation of Poison Frogs.

In animals, bright colors often evolve to mimic other species when a resemblance is selectively favored. Understanding the proximate mechanisms underlying such color mimicry can give insights into how mimicry evolves-for example, whether color convergence evolves from a shared set of mechanisms or through the evolution of novel color production mechanisms. We studied color production mechanisms in poison frogs (Dendrobatidae), focusing on the mimicry complex of Ranitomeya imitator. Using reflectance spectrometry, skin pigment analysis, electron microscopy, and color modeling, we found that the bright colors of these frogs, both within and outside the mimicry complex, are largely structural and produced by iridophores but that color production depends crucially on interactions with pigments. Color variation and mimicry are regulated predominantly by iridophore platelet thickness and, to a lesser extent, concentration of the red pteridine pigment drosopterin. Compared with each of the four morphs of model species that it resembles, R. imitator displays greater variation in both structural and pigmentary mechanisms, which may have facilitated phenotypic divergence in this species. Analyses of nonmimetic dendrobatids in other genera demonstrate that these mechanisms are widespread within the family and that poison frogs share a complex physiological "color palette" that can produce diverse and highly reflective colors.

Employment of baby sign language in interactive learning for Hearing-impaired people in Malaysia

<span lang="EN-GB">Communication is very important for people including adults and babies to exchange their thoughts, views and information. They can express their thoughts verbally and non-verbally. Baby sign language is non-verbal type that consists of a set of standard and organised form of hand gestures motion to represent different word. This language allows the children who have hearing impairment problem to communicate with others. In addition, it can also be taught to normal babies to convey their need before they have the ability to speak. It is very challenging for adults to understand babies and children with hearing impaired problem. Thus, a real-time hand gesture recognition system has been developed for adult to interactively learn the baby sign language. Firstly, the face region was detected in order to use the skin colour model of the face to detect the hand. After that, the hand shape was determined by finding the palm centre and convex hull. Convexity defects which representing gaps between the fingers were used to determine the number of fingertips whereas the movement of the hand was detected based on motion analysis. By setting the background colour to blue and adding extra light in front of the laptop, the system successfully obtained an accuracy higher than 85% rate for sign “mom”, “eat” and “milk” when tested on Malay, Chinese and Indian races in Malaysia.</span>

New Algorithm for Palm Print Detection

Advances in technology are increasing the demand for the biometric technology of palm print detection. This work presents a region of interest segmentation algorithm for palm prints based on Hue Saturation Brightness. The aim of this work was to detect palm print region of interest based on skin color and geometric features. After a color palm image is read as a JPEG file, the algorithm passes through three main stages of preprocessing: palm localization, region of interest localization and region of interest extraction. The preprocessing stage consists of three steps: conversion to Hue Saturation Brightness, skin color modeling, and de-noising. The second main stage of palm localization consists of clipping from the left, from the right, from the top and from bottom. The third stage consists of the two steps of determining and extracting region of interest. The algorithm was tested on a dataset of 180 palm images from the Institute of Automation Chinese Academy of Sciences dataset, which contains an equal number of right-hand and left-hand palm images. The results showed that the Hue Saturation Brightness color space for skin detection was superior to other recognition methods, with an accuracy rate of 98.8%.

Multi-face detection based on improved YCbCr color space

Face recognition has become a popular means of recognition. It is required in image retrieval, video monitoring, entrance and exit control of military bases or other sensitive areas and other occasions. Face recognition has three key steps: face detection, feature extraction and feature matching. Face detection plays an important role in face recognition. Skin color segmentation is an important part of face detection. The traditional skin color modeling rules of YCbCr color space still have some defects, so we made some improvements. We changed the parameters of the conversion matrix used in the conversion from RGB space to YCbCr color space, so that the chosen skin color region is closer to the face skin color region. By this way, the system can detect the face most accurately.

Static gesture segmentation technique based on improved Sobel operator

The gesture recognition technology based on computer vision can provide a more natural way of human–computer interaction, and become a research hotspot in the field of gesture recognition. This study proposes an improved Sobel operator which is used for static hand contour extraction. The method is based on the noise existing in the picture, and a bilateral filtering algorithm is employed to denoise and preserve the edges to a large extent. Based on the colour space, the elliptical skin colour model is used to extract the skin colour domain, and the hand is separated according to the block domain feature. Finally, in order to solve the oblique indirection problem of the traditional Sobel operator, two oblique direction templates are added and the weights are reassigned. Then the improved Sobel operator is used to extract the gesture edges, and compared with the classical extraction edge operator Laplace and Canny extraction effects. The experimental results show that the proposed algorithm has high efficiency and accuracy in static gesture segmentation in a simple environment.

Hand Gesture Recognition Using PCA, KNN and SVM

To interact with world using expressions or body movements is comparatively effective than just speaking. Gesture recognition can be a better way to convey meaningful information. Communication through gestures has been widely used by humans to express their thoughts and feelings. Gestures can be performed with any body part like head, face, hands and arms but most predominantly hand is use to perform gestures, Hand Gesture Recognition have been widely accepted for numerous applications such as human computer interactions, robotics, sign language recognition, etc. This paper focuses on bare hand gesture recognition system by proposing a scheme using a database-driven hand gesture recognition based upon skin color model approach and thresholding approach along with an effective template matching with can be effectively used for human robotics applications and similar other applications .Initially, hand region is segmented by applying skin color model in YCbCr color space. Y represents the luminance and Cb and Cr represents chrominance. In the next stage Otsu thresholding is applied to separate foreground and background. Finally, template based matching technique is developed using Principal Component Analysis (PCA), k-nearest neighbour (KNN) and Support Vector Machine (SVM) for recognition. KNN is used for statistical estimation and pattern recognition. SVM can be used for classification or regression problems.

Research on key technologies of intelligent transportation based on image recognition and anti-fatigue driving

Intelligent transportation system needs to solve the main problems in traffic safety. This paper focuses on the traffic safety caused by fatigue driving based on image recognition of key technologies for research and analysis. This paper proposes that the location of face and facial feature points and the classification of fatigue detection are the key links to determine the fatigue driving detection rate. In the analysis of face localization algorithm based on skin color modeling, a corner-based optimization method is proposed to optimize the face region. Based on the analysis of the binary algorithm of human eye localization algorithm, a bi-directional integral projection method is proposed to achieve accurate human eye localization. Then the commonly used fatigue classification algorithm (KNN algorithm) is analyzed. Finally, the proposed method is verified by the simulation test of fatigue driving. Experimental results show that the algorithm based on skin color modeling can accurately locate the driver’s face region. The eye location algorithm based on the two-valued algorithm can also locate the eye location of the tester accurately. The accuracy of KNN fatigue detection model is 87.82%. It can identify driver’s fatigue state with high accuracy.

ANALISIS IDENTIFIKASI WAJAH MENGGUNAKAN GABOR FILTER DAN SKIN MODEL

Abstract - Identification of faces in digital images is a complex process and requires a combination of various methods. The complexity of facial identification is increasing along with the increasing need for high accuracy of facial images. This research analyzes the combination of Skin Color Model and Gabor Filters in the process of identifying facial identities in digital images. The Skin Color Model method is used to separate the face area from facial images based on skin color values on facial images. The face area is then extracted using Gabor Filter. This research resulted in the highest accuracy was 93.6349%. and the lowest accuracy is around 82.45%. The implementation of a combination of Skin Color Models and Gabor Filters can be an alternative method of identifying faces in digital images. Keywords - Digital Image, Face Identification, Skin Color Model, Gabor Filter.

Adaptive Skin Color Detection through Iterative Illuminant Color Estimation and Conversion for Preferred Skin Color Reproduction

A robust skin color detection method using adaptive skin color modeling on YES color space is proposed. The skin colors are classified into three skin regions according to the Y value, and each region is modeled as the skin color in ES plane. By introducing an iterative estimation method of the illuminant color, we effectively remove the pseudo-skin colors that have been erroneously detected in skin color using the standard deviation of E-histogram. We also improve the skin color detection rate using a post-processing expansion mask that can include highlight and shadow pixels near the skin region as the skin color region.

Human Detection in Intelligent Video Surveillance: A Review

With the rapid development of networked video surveillance systems, human detection is essential. These tasks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, such as security and surveillance. This review paper extensively surveys the current progress made toward human detection in intelligent video surveillance. The algorithms presented in this paper are classified as either human detection without classifier training or human detection with classifier training. In the core techniques of human detection without classifier training, three critical processing stages are discussed including background subtraction, Gaussian mixture model (GMM) and skin color model. In the core techniques of human detection with classifier training, two main types are mentioned including holistic human detector, and part-based human detector. Our survey aims to address existing problems, challenges and future research directions based on the analyses of the current progress made toward human detection techniques in computer vision.

A survey on skin detection in colored images

Color is an efficient feature for object detection as it has the advantage of being invariant to changes in scaling, rotation, and partial occlusion. Skin color detection is an essential required step in various applications related to computer vision. The rapidly-growing research in human skin detection is based on the premise that information about individuals, intent, mode, and image contents can be extracted from colored images, and computers can then respond in an appropriate manner. Detecting human skin in complex images has proven to be a challenging problem because skin color can vary dramatically in its appearance due to many factors such as illumination, race, aging, imaging conditions, and complex background. However, many methods have been developed to deal with skin detection problem in color images. The purpose of this study is to provide an up-to-date survey on skin color modeling and detection methods. We also discuss relevant issues such as color spaces, cost and risks, databases, testing, and benchmarking. After investigating these methods and identifying their strengths and limitations, we conclude with several implications for future direction.

Adaptive Skin Colour Modelling for Hand and Face Segmentation

Adaptive Skin Colour Modelling for Hand and Face Segmentation

IDENTIFIKASI WAJAH BERBASIS SEGMENTASI WARNA KULIT WAJAH MENGGUNAKAN NAIVE BAYES CLASSIFIER

Face Identification is an important part in digital image processing techniques to determine the size, location and image value. Face detection is a step in the facial recognition system used for personal identification, monitoring system, criminal law, human and computer interaction and so on. This study featuring face detection using Naïve Bayes Classifier with red, green and blue (RGB) model features. This study uses images with variation of facial expression from training data and testing data. The first step is to create a model of facial skin color with RGB model feature which has done normalization process bygrayscaling image, binarying image and filtering image using max filter, then looking for average RGB color of facial skin, followed by building Gaussian distribution showing skin color classification and processed by Naïve method Bayes Classifier (NBC) in determining the classification result.From the test results with a total of 70 test data and a threshold of 0.1, the results of system accuracy reaches 90.25%.

Sign language recognition in complex background scene based on adaptive skin colour modelling and support vector machine

With the advances of wearable cameras, the user can record the first-person view videos for gesture recognition or even sign language recognition to help the deaf or hard of hearing people communicate with others. In this paper, we propose a purely vision-based sign language recognition system which can be used in complex background scene. We design an adaptive skin colour modelling method for hand segmentation so that the hand contour can be derived more accurately even when different users use our system in various light conditions. Four kinds of feature descriptors are integrated to describe the contours and the salient points of hand gestures, and support vector machine (SVM) is applied to classify hand gestures. Our recognition method is evaluated by two datasets: 1) the CSL dataset collected by ourselves in which images were captured in three different environments including complex background; 2) the public ASL dataset, in which images of the same gesture were captured in different lighting conditions. The proposed recognition method achieves acceptable accuracy rates of 100.0% and 94.0% for the CSL and ASL datasets, respectively.

Sign language recognition in complex background scene based on adaptive skin colour modelling and support vector machine

With the advances of wearable cameras, the user can record the first-person view videos for gesture recognition or even sign language recognition to help the deaf or hard of hearing people communicate with others. In this paper, we propose a purely vision-based sign language recognition system which can be used in complex background scene. We design an adaptive skin colour modelling method for hand segmentation so that the hand contour can be derived more accurately even when different users use our system in various light conditions. Four kinds of feature descriptors are integrated to describe the contours and the salient points of hand gestures, and support vector machine (SVM) is applied to classify hand gestures. Our recognition method is evaluated by two datasets: 1) the CSL dataset collected by ourselves in which images were captured in three different environments including complex background; 2) the public ASL dataset, in which images of the same gesture were captured in different lighting conditions. The proposed recognition method achieves acceptable accuracy rates of 100.0% and 94.0% for the CSL and ASL datasets, respectively.

Research on Driving Fatigue Detection Based on PERCLOS

This paper expounded the mechanism on PERCLOS detecting and evaluating fatigue driving, and briefly introduced the composition of hardware evaluation system. This system firstly detected the face region roughly using skin-color model. Then the drivers’ eyes were located exactly on RGB by face geometry features. Finally, a judgment of the drive’s fatigue condition was made according to the number of white pixels in the eyes area and the duration of driver’s eyes closing. Experiments showed that the system could accurately locate eyes and the precision rate was 93.3%.

Adaptive skin detection using face location and facial structure estimation

Reliable and accurate facial skin extraction is the most critical and urgent issue for adaptive skin detection. Aiming at resolving this issue, the authors propose an adaptive skin detection method using face location and facial structure estimation. The face location algorithm is developed to improve the reliability of face detection and extract a face region with a high proportion of skin. Facial structure estimation is exploited to further reduce the impact of non-skin factors on dynamic skin colour modelling. The colour space distribution model of extracted facial skin is very close to that of real facial skin. Finally, the skin in an image is obtained by using a hybrid colour space strategy. Extensive experimental comparisons with some state-of-the-art methods have shown the superior performance of the proposed method.

A Hybrid Approach for Real-Time Object Detection and Tracking to Cover Background Turbulence Problem

Objectives: A number of techniques available in literature do not discuss the problem of incorrect object detection in the scenes having unstable or moving background. A novel hybrid technique is proposed in the paper to cover this problem. Methods/Statistical Analysis: The new approach proposed is the hybrid of two well known techniques for object detection. One is frame-differencing approach and other is skin colour modelling. This newer technique exploits the fact that the demerit of one technique works as merit for other and hence hybrid technique resolves the problem of moving background or turbulence in background. A real time video having turbulence in background is used for testing the efficiency of the approach. Findings: With accuracy of 0.97, the proposed approach outperforms the individual approaches i.e. frame-differencing and skin colour modelling. A very low value of False Positive Rate (FPR) for proposed approach compared to other approaches confirms the least incorrect detections. High value of True Positive Rate (TPR) conveys that fall out of correct object is least in the video by proposed approach. Results show that the proposed approach better applicable for background with turbulence. Application/Improvements: Automatic object detection and tracking in applications as surveillance are tractable with the proposed hybrid approach.