Skin Pixels Research Articles

Fully automated unsupervised learning approach for thermal camera calibration and an accurate COVID-19 human temperature tracking

During the past three years, people have suffered greatly from what the World Health Organization called the emerging COVID-19. The world lacked the means and methods for early detection of this virus. Several traditional methods were used for detecting the virus, such as thermometers, remote thermal detection guns, and other conventional methods. Most of these systems monopolized making profits or selling their camera products, with prices for these cameras equipped with temperature detection systems exceeding three thousand dollars. An unsupervised model for real-time detection of thermal face skin temperature is proposed. Despite the scarcity and limited availability of thermal video data, we found and used a database created at Nazarbayev University in Nur-Sultan, Kazakhstan, which contains clips of thermal video and RGB video. The two different videos were calibrated, and the unity was measured by two metrics: SSIM and correlation. Four methods of registration were used to achieve perfect congruence, and congruence was also measured through the two previous metrics. The K-means method was then used to extract clusters, and functions for post-processing were built. The thermal face skin was extracted by multiplying the binary face mask with the thermal face, and the temperature of the face was calculated by taking the average values of the thermal face skin pixels and converting them from Fahrenheit to Celsius. Satisfactory results were obtained, with some temperatures detected within the normal range, others below the normal range, and others higher than this rate.

Evaluation of transformation invariant loss function with distance equilibrium in prediction of imaging photoplethysmography characteristics

Objective. Monitoring changes in human heart rate variability (HRV) holds significant importance for protecting life and health. Studies have shown that Imaging Photoplethysmography (IPPG) based on ordinary color cameras can detect the color change of the skin pixel caused by cardiopulmonary system. Most researchers employed deep learning IPPG algorithms to extract the blood volume pulse (BVP) signal, analyzing it predominantly through the heart rate (HR). However, this approach often overlooks the inherent intricate time-frequency domain characteristics in the BVP signal, which cannot be comprehensively deduced solely from HR. The analysis of HRV metrics through the BVP signal is imperative. Approach. In this paper, the transformation invariant loss function with distance equilibrium (TIDLE) loss function is applied to IPPG for the first time, and the details of BVP signal can be recovered better. In detail, TIDLE is tested in four commonly used IPPG deep learning models, which are DeepPhys, EfficientPhys, Physnet and TS_CAN, and compared with other three loss functions, which are mean absolute error (MAE), mean square error (MSE), Neg Pearson Coefficient correlation (NPCC). Main results. The experiments demonstrate that MAE and MSE exhibit suboptimal performance in predicting LF/HF across the four models, achieving the Statistic of Mean Absolute Error (MAES) of 25.94% and 34.05%, respectively. In contrast, NPCC and TIDLE yielded more favorable results at 13.51% and 11.35%, respectively. Taking into consideration the morphological characteristics of the BVP signal, on the two optimal models for predicting HRV metrics, namely DeepPhys and TS_CAN, the Pearson coefficients for the BVP signals predicted by TIDLE in comparison to the gold-standard BVP signals achieved values of 0.627 and 0.605, respectively. In contrast, the results based on NPCC were notably lower, at only 0.545 and 0.533, respectively. Significance. This paper contributes significantly to the effective restoration of the morphology and frequency domain characteristics of the BVP signal.

An Optimized Semantic Segmentation Framework for Human Skin Detection

The study incorporating optimization strategy in semantic segmentation is underexplored in dermatology. Existing approaches used complex and various heuristic designs of image processing algorithms and deep models customized for skin detection problems. This paper demonstrates Particle Swarm Optimization (PSO)-incorporated AlexNet framework for the skin segmentation task. The results from testing the trained model are promising. The model produced satisfactory performances even with a strict split of 50 %, confirming the high efficiency of the proposed framework. The mean Jaccard index and Dice similarity measures evaluated between the annotated and predicted mask ranged from 0.80 to 0.93 in the binary classification of pixels as “skin” versus “background”. This work identified that the location and color variability of skin pixels in the training data are crucial to obtaining a good skin segmentation performance. Further works that can be explored in this area include adopting a robust preprocessing strategy to increase data variability and improve model generalization or implementing an optimization-enhanced strategy on the existing segmentation models for comparison.

Two-Stage Method for Clothing Feature Detection

The rapid expansion of e-commerce, particularly in the clothing sector, has led to a significant demand for an effective clothing industry. This study presents a novel two-stage image recognition method. Our approach distinctively combines human keypoint detection, object detection, and classification methods into a two-stage structure. Initially, we utilize open-source libraries, namely OpenPose and Dlib, for accurate human keypoint detection, followed by a custom cropping logic for extracting body part boxes. In the second stage, we employ a blend of Harris Corner, Canny Edge, and skin pixel detection integrated with VGG16 and support vector machine (SVM) models. This configuration allows the bounding boxes to identify ten unique attributes, encompassing facial features and detailed aspects of clothing. Conclusively, the experiment yielded an overall recognition accuracy of 81.4% for tops and 85.72% for bottoms, highlighting the efficacy of the applied methodologies in garment categorization.

Development of contactless human vital signs monitoring device with remote-photoplethysmography using adaptive region-of-interest and hybrid processing methods

Vital sign assessment is an examination that indicates changes in health. Direct contact during vital signs assessment can increase the risk of disease transmission. This research aimed to develop a contactless vital sign monitoring prototype that includes heart rate, respiratory rate, blood pressure, and oxygen saturation using a digital camera based on remote photoplethysmography with an adaptive region of interest. The adaptive region-of-interest method uses face detection and skin segmentation to generate red-green-blue signals, taking only the skin pixels of the patients while also minimising the effect of motion artefacts. The hybrid processing method combines several vital sign extraction methods to filter external irrelevant factors and produce heart rate, respiratory rate, blood pressure, and blood oxygen saturation values. In addition, the prototype was tested on 50 participants using standard vital sign assessment tools for comparison. The technical specification test of the prototype concluded that the optimal distance of this prototype was up to 2 m with a processing time of 2 s for every 1-s video. The vital signs results were presented using Bland-Altman, which showed that although the Bland-Altman plots revealed a substantial variance in the limits of agreement (±15–20 mmHg for blood pressure, ±15–17 bpm for heart rate, ±4–6 bpm for respiratory rate, and ±1–3 % for blood oxygen saturation), the mean differences for all vital signs were small (±0.7–5 mmHg for blood pressure, ±0.4–0.6 bpm for heart rate, ±0.5–0.7 bpm for respiratory rate, ±0.4–0.6 for blood oxygen saturation) and most data points were within the limits. While further clinical studies are needed to assess its reliability in monitoring specific medical conditions, the prototype has shown an acceptable agreement in assessing vital signs compared to the conventional methods, making it feasible for further development into a medical device.

Crowdsourcing Skin Demarcations of Chronic Graft-Versus-Host Disease in Patient Photographs: Training Versus Performance Study.

Chronic graft-versus-host disease (cGVHD) is a significant cause of long-term morbidity and mortality in patients after allogeneic hematopoietic cell transplantation. Skin is the most commonly affected organ, and visual assessment of cGVHD can have low reliability. Crowdsourcing data from nonexpert participants has been used for numerous medical applications, including image labeling and segmentation tasks. This study aimed to assess the ability of crowds of nonexpert raters-individuals without any prior training for identifying or marking cGHVD-to demarcate photos of cGVHD-affected skin. We also studied the effect of training and feedback on crowd performance. Using a Canfield Vectra H1 3D camera, 360 photographs of the skin of 36 patients with cGVHD were taken. Ground truth demarcations were provided in 3D by a trained expert and reviewed by a board-certified dermatologist. In total, 3000 2D images (projections from various angles) were created for crowd demarcation through the DiagnosUs mobile app. Raters were split into high and low feedback groups. The performances of 4 different crowds of nonexperts were analyzed, including 17 raters per image for the low and high feedback groups, 32-35 raters per image for the low feedback group, and the top 5 performers for each image from the low feedback group. Across 8 demarcation competitions, 130 raters were recruited to the high feedback group and 161 to the low feedback group. This resulted in a total of 54,887 individual demarcations from the high feedback group and 78,967 from the low feedback group. The nonexpert crowds achieved good overall performance for segmenting cGVHD-affected skin with minimal training, achieving a median surface area error of less than 12% of skin pixels for all crowds in both the high and low feedback groups. The low feedback crowds performed slightly poorer than the high feedback crowd, even when a larger crowd was used. Tracking the 5 most reliable raters from the low feedback group for each image recovered a performance similar to that of the high feedback crowd. Higher variability between raters for a given image was not found to correlate with lower performance of the crowd consensus demarcation and cannot therefore be used as a measure of reliability. No significant learning was observed during the task as more photos and feedback were seen. Crowds of nonexpert raters can demarcate cGVHD images with good overall performance. Tracking the top 5 most reliable raters provided optimal results, obtaining the best performance with the lowest number of expert demarcations required for adequate training. However, the agreement amongst individual nonexperts does not help predict whether the crowd has provided an accurate result. Future work should explore the performance of crowdsourcing in standard clinical photos and further methods to estimate the reliability of consensus demarcations.

Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images.

Breast cancer is a significant health concern for women, emphasizing the need for early detection. This research focuses on developing a computer system for asymmetry detection in mammographic images, employing two critical approaches: Dynamic Time Warping (DTW) for shape analysis and the Growing Seed Region (GSR) method for breast skin segmentation. The methodology involves processing mammograms in DICOM format. In the morphological study, a centroid-based mask is computed using extracted images from DICOM files. Distances between the centroid and the breast perimeter are then calculated to assess similarity through Dynamic Time Warping analysis. For skin thickness asymmetry identification, a seed is initially set on skin pixels and expanded based on intensity and depth similarities. The DTW analysis achieves an accuracy of 83%, correctly identifying 23 possible asymmetry cases out of 20 ground truth cases. The GRS method is validated using Average Symmetric Surface Distance and Relative Volumetric metrics, yielding similarities of 90.47% and 66.66%, respectively, for asymmetry cases compared to 182 ground truth segmented images, successfully identifying 35 patients with potential skin asymmetry. Additionally, a Graphical User Interface is designed to facilitate the insertion of DICOM files and provide visual representations of asymmetrical findings for validation and accessibility by physicians.

Weakly supervised human skin segmentation using guidance attention mechanisms

Human skin segmentation is a crucial task in computer vision and biometric systems, yet it poses several challenges such as variability in skin colour, pose, and illumination. This paper presents a robust data-driven skin segmentation method for a single image that addresses these challenges through the integration of contextual information and efficient network design. In addition to robustness and accuracy, the integration into real-time systems requires a careful balance between computational power, speed, and performance. The proposed method incorporates two attention modules, Body Attention and Skin Attention, that utilize contextual information to improve segmentation results. These modules draw attention to the desired areas, focusing on the body boundaries and skin pixels, respectively. Additionally, an efficient network architecture is employed in the encoder part to minimize computational power while retaining high performance. To handle the issue of noisy labels in skin datasets, the proposed method uses a weakly supervised training strategy, relying on the Skin Attention module. The results of this study demonstrate that the proposed method is comparable to, or outperforms, state-of-the-art methods on benchmark datasets.

Improving Remote Photoplethysmography Performance through Deep-Learning-Based Real-Time Skin Segmentation Network

In recent years, health-monitoring systems have become increasingly important in the medical and safety fields, including patient and driver monitoring. Remote photoplethysmography is an approach that captures blood flow changes due to cardiac activity by utilizing a camera to measure transmitted or reflected light through the skin, but it has limitations in its sensitivity to changes in illumination and motion. Moreover, remote photoplethysmography signals measured from nonskin regions are unreliable, leading to inaccurate remote photoplethysmography estimation. In this study, we propose Skin-SegNet, a network that minimizes noise factors and improves pulse signal quality through precise skin segmentation. Skin-SegNet separates skin pixels and nonskin pixels, as well as accessories such as glasses and hair, through training on facial structural elements and skin textures. Additionally, Skin-SegNet reduces model parameters using an information blocking decoder and spatial squeeze module, achieving a fast inference time of 15 ms on an Intel i9 CPU. For verification, we evaluated Skin-SegNet using the PURE dataset, which consists of heart rate measurements from various environments. When compared to other skin segmentation methods with similar inference speeds, Skin-SegNet demonstrated a mean absolute percentage error of 1.18%, showing an improvement of approximately 60% compared to the 4.48% error rate of the other methods. The result even exhibits better performance, with only 0.019 million parameters, in comparison to DeepLabV3+, which has 5.22 million model parameters. Consequently, Skin-SegNet is expected to be employed as an effective preprocessing technique for facilitating efficient remote photoplethysmography on low-spec computing devices.

A Study of Machine Learning Regression Techniques for Non-Contact SpO2 Estimation from Infrared Motion-Magnified Facial Video

This work explores the use of infrared low-cost cameras for monitoring peripheral oxygen saturation (SpO2), a vital sign that is particularly important for individuals with fragile health, such as the elderly. The development of contactless SpO2 monitoring utilizing RGB cameras has already proven successful. This study utilizes the Eulerian Video Magnification (EVM) technique to enhance minor variations in skin pixel intensity in particular facial regions. More specifically, the emphasis in this study is in the utilization of infrared cameras, in order to explore the possibility of contactless SpO2 monitoring under low-light or night-time conditions. Many different methods were employed for regression. A study of machine learning regression methods was performed, including a Generalized Additive Model (GAM) and an Extra Trees Regressor, based on 12 novel features extracted from the extracted amplified photoplethysmography (PPG) signal. Deep learning methods were also explored, including a 3D Convolution Neural Network (CNN) and a Video Vision Transformer (ViViT) architecture on the amplified forehead/cheeks video. The estimated SpO2 values of the best performing method reach a low root mean squared error of 1.331 and an R2 score of 0.465 that fall within the acceptable range for these applications.

Segmentasi Warna Kulit Menggunakan Ruang Warna YCBCR Untuk Deteksi Wajah Manusia

Image processing is a system where the process is done by entering in the form of imagery and the result is also in the form of imagery. At first this image processing was done to improve the quality of the image, but with the development of the world of computing which is characterized by increasing the capacity and speed of computer processes. In this research image processing will be used for skin color detection which is the initial process in image processing. Success in detecting pixel imagery can be a category of skin or not skin seen from the results of processing processes such as the process of detecting human faces. Skin detection is also a process of finding skin pixels of color in the area of the image or video, this process is usually used as a preprocessing step to find areas that could potentially have a human face. In this study by applying the color space YcbCr we were able to detect faces in humans with a high percentage accuracy rate with a precession percentage of 77%, Recal 87% and an accuracy rate of 72% from the results of 10 image samples.

Image Segmentation for Human Skin Detection

Human skin detection is the main task for various human–computer interaction applications. For this, several computer vision-based approaches have been developed in recent years. However, different events and features can interfere in the segmentation process, such as luminosity conditions, skin tones, complex backgrounds, and image capture equipment. In digital imaging, skin segmentation methods can overcome these challenges or at least part of them. However, the images analyzed follow an application-specific pattern. In this paper, we present an approach that uses a set of methods to segment skin and non-skin pixels in images from uncontrolled or unknown environments. Our main result is the ability to segment skin and non-skin pixels in digital images from a non-restrained capture environment. Thus, it overcomes several challenges, such as lighting conditions, compression, and scene complexity. By applying a segmented image examination approach, we determine the proportion of skin pixels present in the image by considering only the objects of interest (i.e., the people). In addition, this segmented analysis can generate independent information regarding each part of the human body. The proposed solution produces a dataset composed of a combination of other datasets present in the literature, which enables the construction of a heterogeneous set of images.

An Improved Probability Density Function (PDF) for Face Skin Detection

Face Detection by skin color in the field of computer vision is a difficult challenge. Detection of human skin focuses on the identification of pixels and skin-colored areas of a given picture. Since skin colors are invariant in orientation and size and rapid to process, they are used in the identification of human skin. In addition features like ethnicity, sensor, optics and lighting conditions that are different are sensitive factors for the relationship between surface colors and lighting (an issue that is strongly related to color stability). This paper presents a new technique for face detection based on human skin. Three methods of Probability Density Function (PDF) were applied to detect the face by skin color; these are the Extreme Value Distribution Function and the Exponential Distribution Function methods, in addition to a new proposed model, over the HSV (Hue, Saturation, and Value) color space. The suggested technique aims to enhance skin pixel detection and improve the detection accuracy of a colored region in the human skin in a specific photo. The new model has proved to be 96.05% more accurate than the Extreme value distribution function and Exponential distribution function according to the selected region of the face during experiments. The images used in this paper were 380 color images from CalTech (California Technology Institute) dataset.

Detection and segmentation of melanoma skin cancer in dermoscopy images using modified Alexnet convolutional neural network‐morphological methodology

SummaryMelanoma is most vicious and dangerous type of skin cancer that its early detection can save patients' lives. Computer‐aided methods can be used for this early detection with acceptable performance. In this article, the melanoma skin cancer dermoscopy images are detected and cancer regions are segmented using the proposed modified AlexNet convolutional neural network‐morphological segmentation (CNN‐MS) method. This proposed work consists of enhancement stage, feature matrix construction stage, CNN classification stage, and segmentation stage. In enhancement stage, the skin pixels are contrast enhanced by histogram equalization technique and then non‐sub sampled contourlet transform (NSCT) is applied on the enhanced skin image to obtain the decomposed coefficients. Further, local ternary pattern (LTP) features are computed from the enhanced image and the feature matrix (FM) is constructed by integrating the NSCT coefficients and the LTP features. This constructed FM is fed to the CNN model for the classification of Melanoma skin cancer images from the normal skin images. Finally, morphological functions are applied on the classified melanoma skin image to segment the cancer regions. The performance of this developed model for skin cancer detection is analyzed on international skin imaging collaboration (ISIC) dataset images and achieves 98.11% of Se, 98.5% of Sp, 99.11% of Acc, and 0.96 of MCC. Also, the performance of this developed model for skin cancer detection is analyzed on SIIM‐ISIC dataset images and achieves 98.04% Se, 98.46% Sp, 99.12% Acc, and 0.97 MCC.

Human Skin Tone Detection

Human skin tone variation can be easily seen from person to person within the same ethnic group or different ethnic group. The skin tone is analysed by melanosome size, significant and progressive variation in size and ethnicity. In face recognition the shape of the face gets identified but background used in real time is usually complex with varying lighting conditions. Automatic skin detection has been intensively studied for human-related recognition systems. The Gaussian model for skin detection is with its generality but they lack in their accuracy. Hence we suggest a new statistical colour model for skin detection called an elliptical boundary model. Detection and tracking of human face and hands are used for gesture recognition and to handle the domain transfer problem. The elliptical boundary model overcomes the limitations of the Gaussian model with better performance based on six chrominance spaces on the face, giving a much higher correct detection ratio with faster speed. For that, we are processing with two main domains that are Full stack development which handles the frontend and backend of the application and Machine Learning algorithms which handles the detection of the skin tone. Here we are dealing with image processing techniques for input and the output will find the skin tone of that particular image that we are processing. The applications that involve skin tone detection are face detection, gesture recognition and image filtering. Hence they overcome the different illumination conditions as different domains of skin pixels. The objective of the project is to detect the skin tone from a human face image with various tone characteristics. We are detecting the basic tone differentiation (Fair, Mild or Dark).

Soft Biometrics Authentication

This manuscript presents the design of a new approach of human skin color authentication. Skin color is one of the most popular soft biometric modalities. Since a soft biometric modality alone cannot reliably authenticate an individual, this new system is designed to combine skin color results with other pure biometric modalities to increase recognition performance. In the classification process, we first perform facial skin detection by segmentation using the thresholding method in the HSV color space. Then, the K-means algorithm of the clustering method is used to determine the dominant colors on the skin pixels in the RGB model. Variations according to the R, G and B components are recorded in a reference model to enable an individual’s identity to be predicted on the basis of 30 clusters. Experimental results are promising and give a false acceptance rate (FAR) of 29.47% and a false rejection rate (FRR) of 70.53%.

Fast Skin Segmentation on Low-Resolution Grayscale Images for Remote PhotoPlethysmoGraphy

Facial skin segmentation is an important preliminary task in many applications, including remote PhotoPlethysmoGraphy (rPPG), which is the problem of estimating the heart activity of a subject just by analyzing a video of their face. By selecting all the subject’s skin surface, a more robust pulse signal could be extracted and analyzed in order to provide an accurate heart activity monitoring. Single-photon avalanche diode cameras have proven to be able to achieve better results in rPPG than traditional cameras. Although this kind of cameras produces accurate photon counts at high frame rate, they are able to capture just grayscale low resolution images. For this reason, in this work, we propose a novel skin segmentation method based on deep learning that is able to precisely localize skin pixels inside a low-resolution grayscale image. Moreover, since the proposed method makes use of depthwise separable convolutional layers, it could achieve real-time performances even when implemented on a small low-powered IoT device.

CİLT SEGMENTASYONUNDA DERİN ÖĞRENME İLE MAKİNE ÖĞRENİMİNİN KARŞILAŞTIRILMASI

In this study, a skin segmentation study is investigated with deep learning methods. The skin segmentation problem is chosen as a case study. The main reason for this is that there are numerous studies on this subject and the abundance of available data sets. In addition, images containing skin pixels contain multiple attributes. That's why human images are very suitable for comparative studies on machine learning and deep learning. In the first stage of this study, skin segmentation will be done by using RGB space, which contains deep information as an attribute in machine learning. At the same time, to show the success of the deep learning algorithm, the effect of deep learning will be tested by converting images to grayscale, and success differences will be given.

Non-contact Measurement of Pulse Rate Variability Using a Webcam and Application to Mental Illness Screening System.

The COVID-19 pandemic is a global health crisis. Mental health is critical in such uncertain situations, particularly when people are required to significantly restrict their movements and change their lifestyles. Under these conditions, many countries have turned to telemedicine to strengthen and expand mental health services. Our research group previously developed a mental illness screening system based on heart rate variability (HRV) analysis, enabling an objective and easy mental health self-check. This screening system cannot be used for telemedicine because it uses electrocardiography (ECG) and contact photoplethysmography (PPG), that are not widely available outside of a clinical setting. The purpose of this study is to enable the extension of the aforementioned system to telemedicine by the application of non-contact PPG using an RGB webcam, also called imaging- photoplethysmography (iPPG). The iPPG measurement errors occur due to changes in the relative position between the camera and the target, and due to changes in light. Conventionally, in image processing, the pixel value of the entire face region is used. We propose skin pixel extraction to eliminate blinks, eye movements, and changes in light and shadow. In signal processing, the green channel signal is conventionally used as a pulse wave owing to the absorption characteristics of blood flow. Taking advantage of the fact that the red and blue channels contain noise, we propose a signal reconstruction method for removing noise and strengthening the signal in the pulse rate variability (PRV) frequency band by weighting the three signals of the RGB camera. We conducted an experiment with 13 healthy subjects, and showed that the PRV index and pulse rate (PR) errors estimated by the proposed method were smaller than those of the conventional method. The correlation coefficients between estimated values by the proposed method and reference values of LF, HF, and PR were 0.86, 0.69, and 0.96, respectively.

A novel approach for human skin detection using convolutional neural network

Human skin detection, which is one of the important pre-processing phases, has a wide range of applications such as face tracking, skin diseases, video surveillance, web content filtering, and so on. Skin detection is a challenging problem because skin color can vary dramatically in its appearance due to many factors such as illumination conditions, pose variations, race, aging, and complex background. Several methods dealing with skin detection assume that skin pixels can be extracted from background colors according to some thresholding rules related to a specific color model. Nevertheless, it is a complex task to recognize skin pixels under the challenging factors aforementioned. In the recent era, the success of deep convolutional neural network (CNN) has strongly influenced the field of computer vision. However, we could find only a few researches that apply deep learning methods to deal with the skin detection problem. This paper presents a novel approach based on CNN for skin detection. Extensive experiments show that the proposed approach exceeds the best result for other state-of-the-art methods.