Skin Color Detection Research Articles

The causal effects of lifestyle, circulating, pigment, and metabolic factors on early age-related macular degeneration: a comprehensive Mendelian randomization study

PurposeEarly detection of lifestyle factors, skin and hair color, circulating parameters, and metabolic comorbidities is crucial for personalized prevention and treatment of early age-related macular degeneration (AMD). This study aimed to assess the relationships between genetically predicted comprehensive risk factors and early AMD.Methods and resultsPublicly available genome-wide association study (GWAS) data were utilized to identify genetic variants significantly associated with each trait. We applied a Bonferroni-corrected significance level of P < 0.0017. P values between 0.0017 and 0.05 were considered suggestive associations. Univariable Mendelian randomization (MR) analyses revealed that elevated serum HDL-C, lower serum TG, and decreased three circulating fatty acids levels were robust indicators of an increased risk of early AMD (all P < 0.0017), with odds ratios (ORs) and 95% confidence intervals (CIs) of 1.218 (1.140–1.303), 0.784 (0.734–0.837), 0.772 (0.698–0.855), 0.776 (0.706–0.852), and 0.877 (0.798–0.963), respectively. Additionally, the “never eat wheat products”, “age started wearing glasses”, and “skin color” were significantly associated with the risk of early AMD (both P < 0.0017), with ORs (95% CIs) of 23.853 (2.731–208.323), 1.605 (1.269–2.030) and 1.190 (1.076–1.317), respectively. Multivariable MR analysis confirmed that elevated serum HDL-C (OR = 1.187, 1.064–1.324) increased the risk of early AMD, while higher serum TG (OR = 0.838, 0.738–0.950) was associated with a significantly lower risk. Furthermore, validation results indicated that serum HDL-C 1.201 (1.101–1.310) and TG 0.795 (0.732–0.864) were significantly associated with the risk of early AMD. There were suggestive associations of smoothies, chronotype, and hair color (0.0017 < P < 0.05), but sun/UV protection, smoking, BMI, diabetes, high blood pressure, cardiovascular diseases, fresh fruit intake, fish oil/cod liver oil supplement, sleeplessness, serum C-reactive protein level, and iron level were not associated with the risk of early AMD.ConclusionsOur comprehensive MR analysis demonstrated that elevated circulating HDL-C levels increase the risk of early AMD, while TG and fatty acid levels are associated with a decreased risk. These findings provide robust evidence for improved diagnosis and personalized prevention and treatment of early AMD.

Research on Mixed Reality Hand Interaction Technology in Flight Simulation Teaching

Abstract The integration of mixed reality simulation training in institutional education and teaching processes can yield significant quality benefits. Non-wearable interactive methods represent a more natural form of hand interaction within flight cockpits, covering various emerging fields such as mixed reality, computer vision, and human-computer interaction. This paper is guided by a “human-centered” approach to natural human-computer interaction and proposes a virtual hand mixed reality interaction scheme based on hand natural feature points. This study introduces a hand detection, segmentation, and recognition algorithm based on skin colour and key hand feature points. Initially, the algorithm uses the Otsu adaptive threshold algorithm in the YCbCr space for skin colour detection to accommodate varying image brightness levels. Subsequently, it employs a hand Keypoint model for rapid hand detection within skin-like areas while excluding interference from other regions. During the process of hand tracking, optimization prediction is performed using the particle filter algorithm combined with the artificial fish swarm algorithm to achieve tracking accuracy within six pixels in both horizontal and vertical directions. Gesture recognition involves extracting Fourier descriptive contour features of the hand bone structure through identification of feature points. Furthermore, this study combines the optimization capabilities of the artificial fish swarm algorithm to enhance support vector machine model parameter optimization for improved recognition rates. Testing involved 800 processed images as part of a test set for gesture recognition; only five commonly used gestures within flight cockpits were recognized and classified with an accuracy rate reaching up to 98%. Finally, this research presents virtual representations of common operational gestures including natural hand states, control stick manipulation, and throttle handling positions, button activations, and rotational movements via head-mounted displays or screens. The proposed mixed reality-based hand gesture interaction system addresses challenges related to complex background interference during hand detection in cockpit environments as well as issues arising from lighting disturbances. Additionally it resolves problems associated with insufficient particles leading to tracking failures while significantly enhancing registration effects during tracking processes. Moreover it mitigates issues related to Fourier descriptive features being influenced by background variations or changes in pose along with limitations pertaining to expressing singular forms of gestures thereby improving overall classification accuracy.

Artificial intelligence ethics: ethical consideration and regulations from theory to practice

&lt;p&gt;&lt;span lang="EN-US"&gt;The advancement of artificial intelligence (AI) has led to its widespread use in sectors such as finance, healthcare, military, and employment in developed countries. However, this reliance has raised concerns about AI governance, particularly regarding algorithmic biases based on skin color, gender, race, and age. Consequently, many countries have introduced regulations and ethical frameworks to address these issues. The Ministry of Digital Economy and Entrepreneurship in Jordan has included AI in its 2022 plan, signaling significant progress. The integration of AI in education programs underscores this commitment. However, addressing AI's potential negative impacts is essential. We propose ethical considerations and regulations for AI to complement Jordan's initiatives. Our research aims to promote responsible AI usage by developing ethical guidelines in Jordan. It presents techniques to identify and mitigate biases related to skin color, gender, and age in AI outputs and datasets. The research includes extensive testing on datasets, analyzing approximately 100 images, and revealing notable error rates, including a 16% error rate in detecting skin color, a 4% error rate in seeing white faces, and a 6% error rate in identifying females over men. Therefore, ethical considerations and regulations for AI applications in Jordan must be implemented.&lt;/span&gt;&lt;/p&gt;

Hand Gestures Recognition Model Using Adaptive Feature Extraction with Attention-Based Hybrid Deep Learning via Optimization Strategy

Research on continuous hand gesture recognition is motivated by applications of deaf people to provide effective communication between one another. Numerous techniques are adapted for effectively identifying the hand gesture. Moreover, the collection of data becomes a challenging task in the existing models. In some instances, hand gesture in low-resolution images becomes a complicated task. In this research work, a deep learning-based hand gesture detection model is designed using hand data points to uncover patterns that are used to convey information. Initially, hand gesture images are obtained from public sources. The collected inputs are preprocessed through Contrast-Limited Adaptive Histogram Equalization and filtering to eliminate the unnecessary blurs in the images. The preprocessed images undergo hand segmentation using four methods color space transformation, skin color detection, active contour, and morphological operation. The segmented image is subjected to data point feature extraction, where the Adaptive Weighted Scale-Invariant Feature Transform is utilized for further enhancement. The hand data point-extracted features are given to the recognition of hand gestures with the support of an attention-based hybrid network, where the Attention-based Hybrid 1D Convolutional Neural Network with Recurrent Neural Network recognizes the hand gestures from the hand data points. In the hybridization network, the values are optimized with the help of the investigated Adaptive Dandelion White Shark Optimizer to enhance recognition effectiveness. The test results are validated with the existing hand gesture recognition models using diverse evaluation metrics. The findings of the recommended method show 97% and 99% in terms of accuracy and NPV. Thus, the developed model is validated to outperform significant performance rather than the existing models.

Hand Gesture Recognition System Based on Indian Sign Language Using SVM and CNN

Hand gestures (HGs) have been extensively acknowledged as a communication standard for the deaf and dumb school system in the Indian sign language (ISL). We suggested an automated Indian sign language recognition (ISLR) method for English in this study. Initially, the hand region is segmented using the Grasshopper optimization algorithm (GOA) based on the skin color model method. The segmentation’s efficacy is further assessed using three techniques: GOA-based skin color detection algorithm (SCDA), particle swarm optimization-based SCDA (PSO-SCDA), and artificial bee colony-based SCDA (ABC-SCDA). Finally, a database for the retrieved motions representing distinct English alphabets is created. For gesture recognition, the system is trained, and a template-based matching approach is used. The classification technique employs the support vector machine (SVM) and the convolution neural network (CNN). Our suggested recognition method achieved the highest accuracy of 97.85% with GOA-SCDA, compared to 89.29% and 93.96% with PSO-SCDA and ABC-SCDA, respectively. Furthermore, CNN outperformed SVM in classification, with a high accuracy of 99.2% and precision of 81.8%.

Bridging the Communication Gap - Sign Language Communication System

Now, imagine a world where words are not spoken, and emotions are not expressed. For the Deaf community, this is not a mere thought experiment but a daily reality. Indeed, they are forced to communicate primarily through sign language e, therefore, regularly feel alienated in a society where words are the primary tool for translating thoughts and feelings. Under such circumstances, most Deaf and hard-of-hearing people are struggling to communicate the most effectively. They usually opt for interpreters or their own signaling, but these alternatives are not the most efficient or effective choices. While signaling is the most intuitive and meaningful alternative, its grammar and semantic variations make it impossible to comprehend for those people who are not within the culture. Therefore, we have also developed a software prototype that could interpret sign language automatically. This paper presents a new method to recognize Indian sign language alphabets A-Z and digits (0-9) in a real- time video feed by employing the Bag of Visual Words model (BOVW). Furthermore, it not only predicts the labels of the signs but also gives the output as text and speech. The segmentation process here comprehends skin color detection and background subtraction. Speeded Up Robust FeaturesSurf features have been extracted again from the images, and histograms are built to map the signs to text labels. Furthermore, we have utilized algorithms such as Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) for binary classification. Finally, we have established an interactive Graphical User Interface (GUI to make this process more user-friendly

An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition

The development of hand gesture recognition systems has gained more attention in recent days, due to its support of modern human-computer interfaces. Moreover, sign language recognition is mainly developed for enabling communication between deaf and dumb people. In conventional works, various image processing techniques like segmentation, optimization, and classification are deployed for hand gesture recognition. Still, it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption, increased false positives, error rate, and misclassification outputs. Hence, this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques. During image segmentation, skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion. Then, the Heuristic Manta-ray Foraging Optimization (HMFO) technique is employed for optimally selecting the features by computing the best fitness value. Moreover, the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate. Finally, an Adaptive Extreme Learning Machine (AELM) based classification technique is employed for predicting the recognition output. During results validation, various evaluation measures have been used to compare the proposed model's performance with other classification approaches.

FPGA implementation of two multilayer perceptron neural network in cascade for efficient real time hand gestures tracking

This paper presents implementation of a hand gestures tracking system with a fully connected multilayer perceptron and a supervised sequential learning algorithm on low cost FPGAs (Field Programmable Gate Array). Two networks are used in the study. The first network is trained to detect skins and the second identifies face and hand gestures. Also, a binary image is made from the first network, which is fed into the second network as input. Skin network has two output classes, and hand network 6. The first network recognizes skin and non-skin images and the other distinguishes hands from faces while hand gestures are tracked and classified. Both networks are trained in Matlab before each was implemented on a separate FPGA and the two FPGAs are then connected to each other. Finally, the overall result is compared. Both networks are implemented on Xilinx Spartan XC3S1000 4fg456. Two hardware-implemented networks are applied to a video sequence frame by frame. Features of both hands are extracted and the network is trained using a constructed vector to track left and right hands. Results show that the implemented system enjoys a suitable trade-off between accuracy and speed, reaching 208 fps with the least memory usage. This method has been compared to other methods, as well as methods in which only one threshold is used to detect skin color.

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.

A Standardized Approach for Skin Detection: Analysis of the Literature and Case Studies

Skin detection involves identifying skin and non-skin areas in a digital image and is commonly used in various applications, such as analyzing hand gestures, tracking body parts, and facial recognition. The process of distinguishing between skin and non-skin regions in a digital image is widely used in a variety of applications, ranging from hand-gesture analysis to body-part tracking to facial recognition. Skin detection is a challenging problem that has received a lot of attention from experts and proposals from the research community in the context of intelligent systems, but the lack of common benchmarks and unified testing protocols has hampered fairness among approaches. Comparisons are very difficult. Recently, the success of deep neural networks has had a major impact on the field of image segmentation detection, resulting in various successful models to date. In this work, we survey the most recent research in this field and propose fair comparisons between approaches, using several different datasets. The main contributions of this work are (i) a comprehensive review of the literature on approaches to skin-color detection and a comparison of approaches that may help researchers and practitioners choose the best method for their application; (ii) a comprehensive list of datasets that report ground truth for skin detection; and (iii) a testing protocol for evaluating and comparing different skin-detection approaches. Moreover, we propose an ensemble of convolutional neural networks and transformers that obtains a state-of-the-art performance.

Clothing Style Recognition and Design by Using Feature Representation and Collaboration Learning

In order to recognize the clothing style, this paper establishes a standard clothing style library. The images of clothing style are provided and annotated by fashion design experts. The clothing style image is represented as a set of line segments that is obtained by detecting the lines and corners consisting of the edge feature points in the image. Then, the authors extract the features of the line segment set and use the extracted features to establish clothing style matching rules to make the system automatically produce the matching and recognizing criteria for the clothing style images. When inputting an image of a person wearing clothes, they first find the position of the person through skin color detection and then locate the clothing. The clothing region is segmented by seed growth algorithm. The features of the segmentation are compared with clothing style matching rules to determine the style. The experimental results show that the recognition rate of clothing style can reach more than 92% for the standard clothing images and more than 91% for real clothing images.

Feasibility of Skin Water Content Imaging Using CMOS Sensors.

Pressure injuries (PI) result from pressure-induced damage to the skin and underlying tissues. Currently, Stage I PI are detected using visual skin assessments. However, this visual method is unable to detect skin color changes in persons with darkly pigmented skin, which results in a higher Stage II-IV PI incidence and PI-associated mortality in persons with a darker complexion. Thus, a more objective method of early-stage PI detection is of great importance. Optical spectroscopy is a promising modality for the noncontact diagnosis and monitoring of skin water content, capable of detecting edema and Stage I PI. The scope of the current study is to assess the feasibility of imaging the water content of the skin using Si-based sensors. We have considered two primary cases: the elevated bulk water content (edema) and localized water pool (e.g., blood vessels). These two cases were analyzed using analytical models. We found that detecting the watercontent contrast associated with edema in tissues is within the reach of Si-based sensors. However, although the effect is expected to be detectable even with consumer-grade cameras, with the current state of technologies, their use in real-world conditions faces numerous technical challenges, mainly due to the narrow dynamic range.

Study on A face detection method based on elliptic skin color model

Human face is a natural structural target with abundant details, and its detection results are easily affected by facial details, expressions and posture changes. In color images, the distribution of skin color is not affected by the changes of facial details, expression and posture, and the speed of skin color detection is very fast. This paper proposes a face detection algorithm based on elliptic skin color model. In YCbCr color space, the elliptic skin color model and logistic regression analysis were used to determine the skin color probability of each point, and the pixels of each point were mapped to [0, 1]. Based on Ostu method, a parallel genetic algorithm was used to determine the threshold of skin color segmentation to segment the face region. The results show that this method improves the speed of face detection and has good robustness to posture and expression changes.

Real Time Skin Color Detection Based on Adaptive HSV Thresholding

The detection of human skin color has been studied extensively during the past two decades. It is an essential task for various computer vision applications such as biometric authentication, face/hands tracking and gesture analysis. New machine learning methods are effective for skin color detection. However, they are not suitable for real time applications since they are computationally heavy. A lightweight approach for skin color detection consists of using segmentation rules extracted by an investigation on skin color distribution. The kin appearance varies with diversity of image types, acquisition parameters and scene illumination. There are no general segmentation rules that provide effective skin segmentation for different scene conditions. In this paper we present a real-time skin color detector which adapts itself according to tracked human parts. First, initial thresholds are calculated using two popular skin datasets. Those thresholds can also be calculated quickly using small training sets. The proposed skin color detector showed comparable skin segmentation to DeepLabV3++ application and an improvement in term of F1 measure when compared to methods that relies on static rules.

Facial Skin Color Segmentation Using Otsu Thresholding Algorithm

The development of technology and information is currently very fast. One of the fields of technology and information that is experiencing development is the field of digital image processing. There are many technologies today that utilize digital images such as facial recognition, object detection and many others. Skin is one of the largest components of the human body. Currently, technology in the identification of skin color is widely used in recognizing the human race. In this study, skin color detection uses the YCbCr color space, which in this study only uses the range of Cb and Cr values, and ignores the Y value. Where Y is the lighting in the image. So if not changed, the image will contain light effects that can change the characteristics of skin color. However, problems were found because the detected images were not segmented properly, such as clothes and hair from the tested images were still detected as skin. Therefore, the HCbCr color space method is proposed where the Hue value will represent the color of visible light. While the Otsu Thresholding method will separate the background from the object in the digital image.

PowerPoint slideshow navigation control with hand gestures using Hidden Markov Model method

Gesture is the easiest and most expressive way of communication between humans and computers, especially gestures that focus on hand and facial movements. Users can use simple gestures to communicate their ideas with a computer without interacting physically. One form of communication between users and machines is in the teaching and learning process in college. One of them is the way the speakers deliver material in the classroom. Most speakers nowadays make use of projectors that project PowerPoint slides from a connected laptop. In running the presentation, the speaker needs to move a slide from one slide to the next or to the previous slide. Therefore, a hand gesture recognition system is needed so it can implement the above interactions. In this study, a PowerPoint navigation control system was built. Digital imaging techniques use a combination of methods. The YCbCr threshold method is used to detect skin color. Furthermore, the morphological method is used to refine the detection results. Then the background subtraction method is used to detect moving objects. The classification method uses the Hidden Markov Model (HMM). With 526 hand images, the result shows that the accuracy of the confusion matrix is 74.5% and the sensitivity is 76.47%. From the accuracy and sensitivity values, it can be concluded that the Hidden Markov Model method can detect gestures quite well as a PowerPoint slide navigation control.

Hand Print Recognition System based on FP-Growth Algorithm

Hand print recognition system received great interest in the recent few years such as human-computer interaction, computer vision, and computer graphics. In this paper, proposed system for recognition human handprint based on FP-growth algorithm, the system consists of three-stage. The first stage the detection algorithm using HSV color space, canny algorithm and contrast enhancement for grayscale. In this stage separate skin area in-handprint image through first HSV color space converting RGB to HSV color space as well as conducting specific rules for determining the skin area. And then applies skin hand segmentation for the split of non skin and skin areas where hand skin color detection. After the hand detection stage, the first stage in edge detection is image smoothing through using a Gaussian filter then converted to a grayscale image after then contrast enhancement is an important step in the algorithm detection hand. Finally applying canny edge detection. The second stage extract features through apply seven moment invariants. The three-stage applying FP-growth algorithm for recognition handprint image. The system which has been proposed utilize handprint images databases, the database proposed a large data-set of human hand images, 11K Hands, that consists of palmar and dorsal sides of the human hand images dataset that collective database from 190 various subject’s handprint images is made publicly obtainable. The handprint recognition system achieved rate of 92.70%.

PKT: fast color-based spatial model for human skin detection

We present a new detection method for color-based object detection, which can improve the performance of learning procedures in terms of speed, accuracy, and efficiency, using spatial inference, and algorithm. We applied the model to human skin detection from an image; however, the method can also work for other machine learning tasks involving image pixels. We propose (1) an improved RGB/HSL human skin color threshold to tackle darker human skin color detection problem. (2), we also present a new rule-based fast algorithm (packed k-dimensional tree --- PKT) that depends on an improved spatial structure for human skin/face detection from colored 2D images. We also implemented a novel packed quad-tree (PQT) to speed up the quad-tree performance in terms of indexing. We compared the proposed system to traditional pixel-by-pixel (PBP)/pixel-wise (PW) operation, and quadtree based procedures. The results show that our proposed spatial structure performs better (with a very low false hit rate, very high precision, and accuracy rate) than most state-of-the-art models.

Real-Time Hand Motion Recognition: A Robust Low-Cost Approach

This study presents a robust, low-cost hand motion recognition approach designed to run on low-end computer systems. Our method detects and tracks hand region using real-time images obtained from a low-resolution camera (i.e. webcam) and is not depended on any training or calibration and is not required any special camera apparatus or selectors. The proposed system involves several image processing techniques such as background subtraction, face detection, skin colour detection and template matching. The experimental results show promising performance under various conditions. The method has a wide range of applications where more natural ways of interaction required, such as virtual reality applications, assistive technologies and simulation.

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

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%&#x0D; Key word: Computer Vision, Segmentation, Object Recognition, Text Recognition, Skin Color Detection, Motion Detection, Disability Application