Skin Color Model Research Articles

Skin Pigment Recognition using Projective Hemoglobin- Melanin Coordinate Measurements

The detection of skin pigment is crucial in the diagnosis of skin diseases and in the evaluation of medical cosmetics and hairdressing. Accuracy in the detection is a basis for the prompt cure of skin diseases. This study presents a method to recognize and measure human skin pigment using Hemoglobin-Melanin (HM) coordinate. The proposed method extracts the skin area through a Gaussian skin-color model estimated from statistical analysis and decomposes the skin area into two pigments of hemoglobin and melanin using an Independent Component Analysis (ICA) algorithm. Then, we divide the two-dimensional (2D) HM coordinate into rectangular bins and compute the location histograms of hemoglobin and melanin for all the bins. We label the skin pigment of hemoglobin, melanin, and normal skin on all bins according to the Bayesian classifier. These bin-based HM projective histograms can quantify the skin pigment and compute the standard deviation on the total quantification of skin pigments surrounding normal skin. We tested our scheme using images taken under different illumination conditions. Several cosmetic coverings were used to test the performance of the proposed method. The experimental results show that the proposed method can detect skin pigments with more accuracy and evaluate cosmetic covering effects more effectively than conventional methods.

Proposing a Novel Three Triangles Geometrical Approach for Face Detection on Occluded Facial Features

This paper is associated with the novel approach for detecting the face and facial features from the images where the facial features are occluded. Face detection is one of the developing trends in the field of biometrics. A lot of face detection algorithm has been developed, but still the face detection rate haven’t reached to 100%. It is due to the presence of various environmental conditions on an image. The occluded facial image is also one of the conditions for high performance face detection. A face is occluded if some area of the face is hidden behind on an object like a mask, sunglass, hand or we can say the face is partially visible. In this paper, we have developed a system, that detects the presence of the facial features and the face from the still images even the facial components are occluded. In this system, we calculate the geometrical position of the facial components by using the facial geometry by calculating the distance and the location of the facial features and the width of the face. The system first extracts the eyes, nose and mouth also known as the facial features by using the skin color model and the Haar cascades. The geometrical model is then applied to the extracted features using the three triangle method that calculates the height, width, distance and the position of the extracted features. Our proposed face detector then detects the presence of the face in an image even with an occlusion. We are developing the face detector for still film image by combining the “three triangle method” with “UKF” and “Haar cascade classifier”, which represents the novelty of our proposed system. We performed our experiments on various still film images under di ff erent conditions, using di ff erent facial databases to clarify the e ff ectiveness of our proposed method.

Skin color model adaptation under varying lighting conditions

Skin region detection is crucial for face recognition, hand tracking, and motion detection. In the detection process, a skin color model is usually required to confine the distribution of skin colors. However, skin color models are sensitive to lighting conditions. Skin segmentation under varying lighting conditions produces poor results. This article presents a skin detection procedure for human–computer interaction sessions under varying lighting conditions. The proposed method requests a skin sample from the user to estimate the color temperature of the light source. Then, the color temperature is used to correct the skin sample. At the subsequent step, the mean of the corrected skin sample is utilized to adapt the skin color model. Finally, the adapted skin color model is employed to segment skin regions in the video stream. Tests using the proposed method and some adaptive skin detection algorithms have been conducted. Statistical data show that the proposed method is superior to color constancy methods and the Gaussian mixture model in skin region segmentation. The proposed method improves the true positive rate by more than 13% in segmenting skin regions of a database. Its true positive rate is 20% better if real-life images are used as test data.

ILUT based Skin Colour Modelling for Human Detection

Objectives: Numerous techniques have been proposed in past for skin colour modelling and some out of these are utilized in human detection problem. This paper proposes a novel technique which uses Indexed Look-up-table (ILUT) for skin colour modelling. Methods: In the proposed technique, skin colour cluster on 2-dimensional Cb-Cr plane in YCbCr colour space is modelled with an ILUT. ILUT contains the lower and upper bounds of Cb values corresponding to each value of Cr in the skin colour cluster. Outliers in the cluster are removed by applying median filter, because they contribute to the wrong classification of skin colour and hence wrong detection. Findings: The proposed technique delivers reasonably good performance on True Positive Rate (TPR), False Positive Rate (FPR) and Accuracy parameters. Classification accuracy of 89.89% for proposed technique is almost comparable to that of other techniques in literature. Comparative results are shown in Table 2 of the paper. Classification complexity is one parameter on which the proposed technique outperforms the rest of the other skin colour modelling techniques and it is of O(MxN). MxN is the image resolution. Skin colour modelling using ILUT is also efficient in terms of space requirements as compared to other non-parametric methods of skin colour modelling. Applications: Least classification complexity makes this technique most appropriate for real time systems/ applications for detecting presence of human being.

A novel approach for face detection using hybrid skin color model

Human face detection has become an area of interest in various biometric applications such as crowd surveillance, human–computer interaction, and many security related areas. It is a major field of current research because there is no deterministic algorithm to find the face(s) in a given image. Face detection is challenging due to varying illumination conditions, pose variations, the complexity of noises and image backgrounds. In this paper, a localized approach for face detection based on skin color segmentation and facial features is proposed. Algorithm proficiently analyzes various skin color models such as RGB, YCbCr, HSV and their combinations for the skin color detection because skin segmentation decreases the computational complexity. The segmented face regions are further classified using a set of facial features such as eye-mouth hole detection, bounding box, and eccentricity ratio. This method is tested on four databases: Bao database contains 157 images, Muct database contains 751 images, FEI database contains 2800 images and GTAV database contains 1188 images. The algorithm achieves an average accuracy of $$97.85~\%$$ . Comparison with Viola Jones, face detection using skin color model, and fast face detection based on skin segmentation and facial features methods has also been done. Further, we apply this method to cartoon characters. Our method works with images with some conditions. First, input images are not monochrome images but color images. Second, the characters skin color has to be near real people skin color. Skin color region is extracted from the image and after this apply the facial features to locate faces in an image. The proposed approach for cartoon characters faces detection performed well and achieves a good accuracy of $$76.00~\%$$ .

Dynamic Hand Gesture Segmentation Method Based on Improved Kalman Filter and Weighted Skin-Color Model

Dynamic Hand Gesture Segmentation Method Based on Improved Kalman Filter and Weighted Skin-Color Model

Colored Image Segmentation using K-Means Algorithm

Hand gestures are natural means of communication for human beings and even more so for hearing and speech impaired people who communicate through sign language. Unfortunately, most people are not familiar with sign language and an interpreter is required to translate dialogues. Hence, there is a need to develop a low cost, easily implementable and efficient means to recognize sign language gestures to eliminate the interpreter and facilitate easier communication. The proposed work achieves a satisfactory recognition accuracy using in-built laptop webcam using combination of 3 skin color models(HSV,RGB,YCbCr) and background subtraction to eliminate noise from webcam low quality images to recognize sign language for helping the hearing and speech impaired in real-time without requiring too much computational power or any other device as it can be implemented in any laptop with a webcam.

Robust skin segmentation using color space switching

Skin detection is very popular and has vast applications among researchers in computer vision and human computer interaction. The skin-color changes beyond comparable limits with considerable change in the nature of the light source. Different properties are taken into account when the colors are represented in different color spaces. However, a unique color space has not been found yet to adjust the needs of all illumination changes that can occur to practically similar objects. Therefore a dynamic skin color model must be constructed for robust skin pixel detection, which can cope with natural changes in illumination. This paper purposes that skin detection in a digital color image can be significantly improved by employing automated color space switching. A system with three robust algorithms has been built based on different color spaces towards automatic skin classification in a 2D image. These algorithms are based on the statistical mean of value of the skin pixels in the image. We also take Bayesian approaches to discriminate between skin-alike and non-skin pixels to avoid noise. This work is tested on a set of images which was captured in varying light conditions from highly illuminated to almost dark.

Faces detection method based on skin color modeling

This study suggests a method to improve the speed of a sliding window type of face detector by way of skin color region detection. The face detection method by way of skin color region detection has been studied in various perspectives: Complicated background images because of the area whose color is similar to the skin color cause high false positive rates. In contrast, the face detection method based on appearance, which adopts a sliding window type, may involve high face detection rates but cause tremendous computational costs in the process of detection scanning as the image size increases, whereas the processing time is also extended accordingly. This study suggests a method to control the subwindow size and detection area of a sliding window by detecting and using the skin color region with the processing time reduced. By means of a face detector with haar wavelet and LBP features, 274 images were collected online in addition to Bao database images, and then an experiment was conducted with them. As a result, the face detection time in utilization of an existing sliding window decreased down to a maximum of 47%.

Fingertip Detection and Gesture Recognition Based on Contour Approximation

In this paper, a new recognition algorithm was developed based on the skin color model and the fingertip structure detection to improve the hand gesture recognition accuracy based on the traditional Hu's moment features. Firstly, the geometric features and the areas of skin were adopted to segment the skin region from the background. The Douglas–Peucker (D–P) algorithm was utilized to conduct the contour approximation to get a polygonal in the process of feature detection. Then the convexity point inspections were conducted in the polygonal. Secondly, we developed two rules for locating and numbering the fingertips. After that, we built a seven-dimensional feature vector. Finally, the hand gesture was recognized by using the distance matching criterion. The developed recognition algorithm improved the recognition accuracy by 2.7% compared with the traditional Hu's moment features. Additionally, it has good robustness in several manners such as shifting, plane rotation and scaling.

Skin Color and the Criminal Justice System: Beyond Black‐White Disparities in Sentencing

This article analyzes sentencing outcomes for black and white men in Georgia. The analysis uses sentencing data collected by the Georgia Department of Corrections (GDC). Among first‐time offenders, both the race‐only models and race and skin color models estimate that, on average, blacks receive sentences that are 4.25 percent higher than those of whites even after controlling for legally‐relevant factors such as the type of crime. However, the skin color model also shows us that this figure hides important intraracial differences in sentence length: while medium‐ and dark‐skinned blacks receive sentences that are about 4.8 percent higher than those of whites, lighter‐skinned blacks receive sentences that are not statistically significantly different from those of whites. After controlling for socioeconomic status in the race‐only and race and skin color models the remaining difference between whites and dark‐ and medium‐skinned blacks increases slightly, to 5.5 percent. These findings are discussed with respect to the implications for public policy and for racial hierarchy in the United States.

Efficient Face Detection Method using Modified Hausdorff Distance Method with C4.5 Classifier and Canny Edge Detection

With the rapid growth of population and technology, security problems become a major issue due to abnormal human behaviors. Recently researches have been motivated towards automatic human face detection from still image or from moving image. Present human face detection system leads computation inaccuracies i.e. higher degree of false negative rate. In this paper, a multilevel hybrid model has been proposed for face detection. In the proposed work, we initially use C4.5 classifier so that foreground and background images can be differentiated, as a result of which search space can be reduced. After that skin color model has been applied to detect the skin region which is followed by canny edge detection to detect the edges of skin region. In the last step, we use the Modified Hausdorff Distance Method which matches the pixel values and detects the faces with lower false negative rate.

Hand Gesture Segmentation Based on Mixed Skin-color Model and FCM Algorithm

Hand Gesture Segmentation Based on Mixed Skin-color Model and FCM Algorithm

A Novel Piece-Wise Linear Algorithm for Human Skin Segmentation

This paper presents a novel method for skin segmentation in color images using piece-wise linear bound skin detection. Various color schemes are investigated and evaluated to find the effect of color space transformation over the skin detection performance. The comprehensive knowledge about the various color spaces helps in skin color modeling evaluation. The absence of the luminance component increases performance, which also supports in finding the appropriate color space for skin detection. The single color component produces the better performance than combined color component and reduces computational complexity.

Face Detection Using Skin Likelihood and Haar Features for Digital Video Processing

Face detection is an important early step in many computer vision systems. By using pixel-wise detectors, spatial analysis of skin probability and skin regions segmentation, a new method for face detection is introduced. In this project, we proposed and implemented a modified self-organizing mixture network (SOMN) which specifies the distribution of objects in image and skin and non skin color model, skin likely-hood to exactly identify skin region of interest from image. Bayesian Decision Rule is applied to specify c as skin color or non skin color. Finally, we are using haar like features to identify face and cascade to improve performance and efficiency. We present results of an extensive experimental study which clearly indicate high competitiveness of the proposed method and its relevance to gesture recognition

Skin region segmentation using an image-adapted colour model

This paper presents a new skin region extraction method that generates an image-adapted skin colour model and then segments skin areas using the model. Our method first detects eyes by using an eye map and then develops an image-adapted skin colour distribution model based on a skin map generated by reliably selecting true skin samples near the detected eyes. All skin areas over the entire image are then segmented with the generated skin model. While most of the existing skin detection methods use some pre-defined colour model, our skin model is adaptively constructed from each test image online so that it can overcome fundamental difficulties in extracting skin areas. Experimental results show that our skin extraction method gives better results as compared to other existing approaches.

A New Face Detection Method Based on Improved Adaboost and Skin Color Model in Complex Background

Two improvements of Adaboost based face detection algorithm are provided in this paper. Firstly, dualthreshold weak classiflers are used to improve the detection accuracy. Secondly, Pearson’s correlation coe‐cient is used as a measurement of correlation between classiflers, which signiflcantly reduces the redundancy features of classic Adaboost algorithm. Through these improvements the detection speed is accelerated signiflcantly. Finally, a new face detection method combining skin color model and improved AdaBoost algorithm is proposed, which is an e‐cient face detection method with high accuracy. Also some experiments are made to certify these improvements.

Self-adaptive algorithm for segmenting skin regions

In this paper, we introduce a new self-adaptive algorithm for segmenting human skin regions in color images. Skin detection and segmentation is an active research topic, and many solutions have been proposed so far, especially concerning skin tone modeling in various color spaces. Such models are used for pixel-based classification, but its accuracy is limited due to high variance and low specificity of human skin color. In many works, skin model adaptation and spatial analysis were reported to improve the final segmentation outcome; however, little attention has been paid so far to the possibilities of combining these two improvement directions. Our contribution lies in learning a local skin color model on the fly, which is subsequently applied to the image to determine the seeds for the spatial analysis. Furthermore, we also take advantage of textural features for computing local propagation costs that are used in the distance transform. The results of an extensive experimental study confirmed that the new method is highly competitive, especially for extracting the hand regions in color images.

Skin color modeling for face detection and segmentation: a review and a new approach

Detecting skin-colored pixels, although seems a straightforward easy task, has proven to be quite a challenging task in images that are captured under complex unconstrained imaging conditions. Color segmentation is an important method of segmenting the body in images. In this paper, we first provides a review of skin color modeling research works for face detection. In particular, we focus on discussing the challenges of skin color modeling, detection, and segmentation, and then we present the skin color modeling and region-based skin segmentation. We also propose a new approach to analyze the hue characteristics of monitoring personnel in coal mine. We determine the skin color boundaries of the miners using the boundary detection method based on the response of visual hue. And then, we apply to determining hue area method using dual hue detector to segment skin color for miners. Experiments show that the method has excellent ability to segment skin regions of miners. We also summarize the most widely used methods for skin detection approaches and collecting their numerical evaluation results.

The System of Face Recognition Design

The technology of face recognition is the media to face images as the identity of the face recognition system.Through the choice of color space and the establishment of skin color model, give a rough detection for the human's image, then use the face Haar features getting more accurate detection.