Skin Detection Research Articles

Gender Classification from Face and Eyes Images Using Deep Learning Algorithm

Gender classification provides additional information about the individual’s identity, which is crucial in surveillance, smart interface, and smart advertising, it is a very important aspect of face analysis that has piqued the interest of researchers in areas such as demographic information collection, surveillance, human-computer interaction, marketing intelligence, security, etc. Usually, facial images are used to extract features for classification. This study aims to determine gender based on the entire face or the eyes (for masked or occlusion faces). The proposed method consists of six main stages: (1) Remove the background using the Deep Labelling Version 3 plus (DeepLabV3+) method; (2) Skin detection by applying the combination of the two colors spaces models (Hue, Saturation, Value (HSV)) and (Luminance, Chrominance blue and Chrominance red (YCbCr)); (3) Face detection using the Haar Cascades classifier method; (4) Face alignment and cropping; (5) Classify the gender. Classification of the gender when the image contains the entire face is based on a deep wavelet. In the case of occlusion faces, we proposed to use a Convolution Neural Network (CNN) for classifying the gender from the eye (s). The dataset used for training this model is the celeb faces attributes dataset (CelebA) and some of the different datasets were also used to compare this model with the other previous works. In addition, we showed that our models perform well with images with some challenges, for example, some of the faces are not fully visible, not completely frontal, wearing glasses with different styles, have low quality and noise, have various lighting, children’s and infants’ faces, with and without makeup, open and closed mouths and closed eyes. Other than that, the accuracy achieved was 98% when classifying from face images and 98% from the eye. The proposed method was compared with previous works and was very promising. The contributions of this proposal were the use of the deep wavelet for gender classification and the proposal of a new method for skin detection, which enhances the performance of the Haar cascade method used for face detection. Also, we proposed a method for face alignment and, finally, the classification of genders with many challenges from the entire face or just from the eye (s).

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.

Quantification of human polyomaviruses MCPyV and HPyV6 in malignant and non-malignant skin lesions

BackgroundHuman Polyomaviruses such as MCPyV and HPyV6 are frequently found as part of healthy skin microbiota and have been associated with Merkel cell carcinoma (MCC), pruritic and dyskeratotic dermatoses, respectively. Their presence in other types of skin conditions varies greatly depending on lesion type and population. ObjectiveTo analyse comparatively the presence of MCPyV and HPyV6 in nonmelanoma skin cancers and healthy skin. MethodsThe authors utilized qPCR techniques to quantify these pathogens in NMSC, premalignant diseases, and healthy skin of 87 patients. ResultsMCPyV was detected in over 40% of samples, while HPyV6 was in 9.6%. MCPyV load was higher in squamous cell carcinomas (SCC) compared to basal cell carcinomas (BCC) (p=0.016) and HPyV6 showed a higher percentage of infected cells in areas of low solar exposure as well as normal skin (p=0.012). A fair agreement (kappa=0.301) was found between MCPyV detection in lesions and their respective perilesional skin, indicating a random process of local dissemination of the virus. Study limitationsThe lack of a larger sampling of different lesion types and protein expression analyses limits the correlation findings. ConclusionThis is the first report of HPyV6 detection in the healthy skin of a Brazilian population, but the role of both polyomaviruses in NMSC has yet to be demonstrated.

A flow-drop electrochemiluminescent design for portable detection of soil and skin 2,4,6-trinitrotoluene

An automatic miniaturized flow-drop electrochemiluminescence (ECL) technique was established by utilizing a coalescing drop as a wall-less electrolytic cell, a self-driven mixer, and a µL scale reactor. Cost-effective ECL detection of 2,4,6-trinitrotoluene (TNT) was proposed, by virtue of the finding of the enhancement on luminol ECL by trace TNT in alkaline aqueous medium. The mechanism of TNT intensifying luminol ECL was suggested as that the one-electron reduction of TNT yielded TNT anion radical converts dissolved oxygen into superoxide anion radical, which subsequently reacted with luminol to produce luminol anion radical, finally resulting in the acceleration of luminol oxidation. By optimizing all the experimental conditions, the intensified ECL was found to be linear to TNT concentration over the 5–100 nM range, with a limit of detection of 0.5 nM. The method’s precision and accuracy were examined via F and t tests. The feasibility of this portable ECL method was validated by determining unknown TNT contents in real soil and finger skin samples via parallel official method. All results support the technique as a robust alternative for on-site TNT detection for environmental and forensic purposes.

Novel neural network model for predicting susceptibility of facial post-inflammatory hyperpigmentation.

To construct a neural network model (ATBP) for predicting susceptibility to Post-inflammatory hyperpigmentation (PIH), which is a rapid, objective, and reliable decision-support method before physical and chemical interventions in dermatology clinics for pigment disorders. A dataset was established based on the VISIA Skin Analysis System detection results of 1953 patients with pigment disorders including 93,477 labeled data under 8 indicators. A novel Post-inflammatory hyperpigmentation susceptibility prediction model incorporating Multi-head self-attention mechanism and Back-propagation neural network is proposed to capture the patterns of skin detection data to predict PIH susceptibility. The results of comparison experiments indicate that Attentive BP (Back Propagation Neural Network) has a significant superiority in prediction accuracy (0.8604) compared with other machine learning models. The ablation experiments prove that the Multi-head self-attention mechanism substantially improves the accuracy and the stability of prediction. The results of the 10-fold cross-validation experiment prove that ATBP is robust and avoids turbulence in predicting. Leveraging Multi-head self-attention mechanism and the architecture advantage of BPNN, the proposed model ATBP obtains the robust and efficient prediction performance in predicting PIH susceptibility via processing large-scale and hi-dimension data, i.e., considering comprehensive skin conditions of individual patient. It can be proved from the experimental results that the proposed model is reliable for decision-support work of PIH susceptibility.

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.

High-fidelity detection, subtyping, and localization of five skin neoplasms using supervised and semi-supervised learning

BackgroundSkin cancers are the most common malignancies diagnosed worldwide. While the early detection and treatment of pre-cancerous and cancerous skin lesions can dramatically improve outcomes, factors such as a global shortage of pathologists, increased workloads, and high rates of diagnostic discordance underscore the need for techniques that improve pathology workflows. Although AI models are now being used to classify lesions from whole slide images (WSIs), diagnostic performance rarely surpasses that of expert pathologists. ObjectivesThe objective of the present study was to create an AI model to detect and classify skin lesions with a higher degree of sensitivity than previously demonstrated, with potential to match and eventually surpass expert pathologists to improve clinical workflows. MethodsWe combined supervised learning (SL) with semi-supervised learning (SSL) to produce an end-to-end multi-level skin detection system that not only detects 5 main types of skin lesions with high sensitivity and specificity, but also subtypes, localizes, and provides margin status to evaluate the proximity of the lesion to non-epidermal margins. The Supervised Training Subset consisted of 2188 random WSIs collected by the PathologyWatch (PW) laboratory between 2013 and 2018, while the Weakly Supervised Subset consisted of 5161 WSIs from daily case specimens. The Validation Set consisted of 250 curated daily case WSIs obtained from the PW tissue archives and included 50 “mimickers”. The Testing Set (3821 WSIs) was composed of non-curated daily case specimens collected from July 20, 2021 to August 20, 2021 from PW laboratories. ResultsThe performance characteristics of our AI model (i.e., Mihm) were assessed retrospectively by running the Testing Set through the Mihm Evaluation Pipeline. Our results show that the sensitivity of Mihm in classifying melanocytic lesions, basal cell carcinoma, and atypical squamous lesions, verruca vulgaris, and seborrheic keratosis was 98.91% (95% CI: 98.27%, 99.55%), 97.24% (95% CI: 96.15%, 98.33%), 95.26% (95% CI: 93.79%, 96.73%), 93.50% (95% CI: 89.14%, 97.86%), and 86.91% (95% CI: 82.13%, 91.69%), respectively. Additionally, our multi-level (i.e., patch-level, ROI-level, and WSI-level) detection algorithm includes a qualitative feature that subtypes lesions, an AI overlay in the front-end digital display that localizes diagnostic ROIs, and reports on margin status by detecting overlap between lesions and non-epidermal tissue margins. ConclusionsOur AI model, developed in collaboration with dermatopathologists, detects 5 skin lesion types with higher sensitivity than previously published AI models, and provides end users with information such as subtyping, localization, and margin status in a front-end digital display. Our end-to-end system has the potential to improve pathology workflows by increasing diagnostic accuracy, expediting the course of patient care, and ultimately improving patient outcomes.

Skin-friendly PVA/PDA/Tyr-PEAm composite hydrogel with long-term antibacterial and self-recovery ability for wearable strain/pressure sensor

Flexible and self-healing conductive hydrogel, as a typical electronic skin with the function of transmitting sensory signals, has attracted remarkable attentions in the biomaterials field. However, the practical application of electronic skin has been severely limited owing to the potential bacterial infection threats. Here, in order to improve the antibacterial properties of electronic skin, a star-shaped polyetheramide (Tyr-PEAm) derived from L-tyrosine was synthesized and cross-linked into the hydrogel network. A compliant and self-healing conductive hydrogel (defined as PVA/PDA/Tyr-PEAm) was assembled from polyvinyl alcohol (PVA), polydopamine (PDA) and Tyr-PEAm. The hydrogel achieved a comprehensive performance of long-term antibacterial activity (the inhibition zones remained unchanged over 7 days), low hemolysis activity (hemolysis rate of 0.042 %), good conductivity and rapid self-healing ability (healing within 1 s). Therefore, the hydrogel could be used not only as a detection device to check normal skin, but also as the wound skin detection in real time. Furthermore, the good conductivity and rapid self-healing ability contributed to the hydrogel to detect various macro and micro movements of the human body, as well as to recycle as a new sensor. The PVA/PDA/Tyr-PEAm hydrogel that integrated intelligent signals and resisted bacterial infections exhibited broad application prospects in biomedical materials.

PO11: MRI for Simultaneous Skin and Applicator Imaging in Surface Brachytherapy

<h3>Purpose</h3> In skin High-Dose-Rate (HDR) brachytherapy, CT is commonly used for surface applicator imaging and treatment planning. However, since CT does not clearly depict skin, lesion depth is estimated from additional ultrasound examinations. MRI offers better soft tissue contrast than CT, and a PETRA MR sequence has recently shown the potential to visualize surface applicators and catheters, producing treatment plans comparable to CT-based ones. Additional skin detection on MR images would provide a complete information set for MR-only treatment planning. We investigated the feasibility of simultaneous skin and surface applicator imaging using MRI. <h3>Materials and Methods</h3> A Freiburg flap applicator (14 catheters with 24 beads each) was wrapped around the right shin of a healthy female volunteer. The volunteer was scanned in a 3T Siemens Vida using an UltraFlexLarge18 coil around and a Spine32 coil under her lower right leg. A 3D Dixon VIBE sequence (TR\TE 4.02\1.32 ms, field-of-view (FOV) 350 × 350 mm², 1.09 × 1.09 × 1.1 mm³ voxels, bandwidth 1116 Hz/pixel, acquisition time 1min 38s) was acquired coronally. Obtained in-phase, opposed-phase, water-only and fat-only image series were assessed for applicator and skin detectability in all three orientations. The series providing best visibility for both features was selected, and skin thickness was measured at five points around the leg (figure top left), on five reconstructed axial slices with distance 17.5 mm from each other, using a linear distance tool in the MIM software. <h3>Results</h3> The 3D properties of the Dixon VIBE sequence allowed for coronal acquisition orientation to best cover the area of interest, with reliable reconstruction in the axial plane employed for treatment planning. Both skin and applicator spheres were visible on the opposed-phase (figure top) and water-only (figure bottom) images in the three orthogonal planes. Opposed-phase images were selected for further analysis due to the consistent sphere signal and clear demarcation of the skin from the underlying bright subcutaneous fat. All applicator beads contained inside the FOV were visualized. Empty plastic catheters produced no signal, but their position in the center of the spheres could be determined by their negative contrast. Average skin thickness from assessed points and slices of the opposed-phase images was 0.18 ± 0.02 mm (mean ± standard deviation). These results lie inside the range of dermal and total skin thickness values reported for extremities of female subjects. The small standard deviation of the mean suggests that measured skin thickness was consistent among measurement locations. <h3>Conclusions</h3> An optimized 3D Dixon VIBE MR sequence offered simultaneous visualization of human skin and a surface brachytherapy applicator for the first time. While PETRA sequences can provide positive catheter contrast, the lack of catheter signal on Dixon VIBE images can also be used to determine their position inside the brighter applicator spheres. Dixon VIBE offered the additional advantage of skin visualization on opposed-phase and water-only series with spatial resolution around 1 mm, providing reasonable skin thickness assessments. As its acquisition time is short, Dixon VIBE can be combined with T2-weighted MR sequences for contouring organs at risk, presenting the possibility to achieve surface brachytherapy treatment simulations with a single imaging modality. Its implementation may pave the path towards MR-only treatment planning in surface HDR brachytherapy.

A comprehensive scheme for tattoo text detection

Tattoo text detection provides a vital clue for person and crime identification. Due to the freestyle and unconstrained nature of handwritten tattoo text over skin regions, accurate tattoo text detection is very challenging. This paper proposes a comprehensive scheme for tattoo text detection which comprises (a) adaptive Deformable Convolutional Neural Network (DCNN) for skin region detection to reduce text detection complexity (b) a Decoupled Gradient Text Detector (DGTD) for tattoo text detection from skin region (c) a Deep Q-Network (DQN) to refine the bounding boxes detected by DGTD, and (d) a Term-Frequency-Inverse-Document-Frequency (TF-IDF) model to group the words into text lines based on semantic information to fix the bounding box for the line. To test the effectiveness, the proposed method is evaluated on different datasets, namely, (i) a newly developed tattoo text dataset, (ii) benchmark bib number dataset of the marathon, and (iii) person re-identification dataset. The proposed method achieves 91.2, 87.5, and 88.8 F-scores from these three respective datasets. To demonstrate its superior performance, the text detection module (without skin detection) is also compared with state-of-the-art scene text detection methods on benchmark datasets, namely, ICDAR 2019 ArT, Total-Text, and DAST1500 and the proposed method achieves 90.3, 88.5 and 89.8 F-score from these respective datasets.

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.

Quantitative evaluation of sensitive skin by ANTERA 3D® combined with GPSkin Barrier®.

At present, there are many different evaluation methods for sensitive skin, including subjective, semisubjective, and objective evaluation. Various objective tests focus on assessing changes in barrier functions. It is anticipated that the ANTERA 3D®, in combination with GPSkin Barrier®, will provide better evaluation of sensitive skin. A total of 20 subjects with sensitive skin and 20 healthy participants were recruited. Sensitive skin subjects were treated with an anti-sensitive moisturizing tolerance-extreme cream which has anti-inflammatory and moisturizing effects, twice daily on the whole face for 28 days. VISIA® Skin Detector was used to record clinical images and red area. GPSkin Barrier® was used to measure TEWL and SCH. Texture, hemoglobin, and influenced area (mm2 ) were recorded using ANTERA 3D® . Subjects underwent skin tests and recorded changes at D0 and D28. Data were only collected from healthy participants who did not receive treatment as controls. TEWL, texture, hemoglobin, and affected area in sensitive skin group were significantly higher than those in healthy group, while SCH was significantly lower than that in healthy group (p all<0.05). After anti-inflammatory and moisturizing treatment, the texture, hemoglobin, and affected area of sensitive skin decreased, TEWL decreased while SCH increased (p all<0.05). Based on the results, the combination of the ANTERA 3D® with GPSkin Barrier® could be used as a new kind of quantitative evaluation method for the detection and diagnosis of sensitive skin.

Differential Diagnosis of Rosacea Using Machine Learning and Dermoscopy.

IntroductionRosacea is a common chronic inflammatory disease occurring on the face, whose diagnosis is mainly based on symptoms and physical signs. Due to some overlap in symptoms and signs with other inflammatory skin diseases, young and inexperienced doctors often make misdiagnoses and missed diagnoses in clinical practices. We analyze the results of skin physiology and dermatoscopy using machine learning method and identify the characteristics of acne rosacea, which differentiate it from other common facial inflammatory skin diseases so as to improve the accuracy of clinical and differential diagnosis of rosacea.MethodsA total of 495 patients who were jointly diagnosed by two experienced doctors were included. Basic data, clinical symptoms, physiological skin detection, and dermatoscopy results were collected, and the clinical characteristics of rosacea and other common facial inflammatory diseases were summarized according to the descriptive analysis results. The model was established using a machine learning method and compared with the judgment results of young and inexperienced doctors to verify whether the model can improve the accuracy of clinical diagnosis and differential diagnosis of rosacea.ResultsThe proportion of yellow and red halos, vascular polygons, as well as follicular pustules, showed by dermatoscopy, and the melanin index in physiological skin detection revealed statistical significance in differentiating rosacea and other common facial inflammatory diseases (all P < 0.01). After adopting the machine learning, we found that GBM (Gradient Boosting Machine) algorithm was the best, and the error rate of this model in the validation set was 5.48%. In the final man-machine comparison, the accuracy of the GBM algorithm model for the classification of skin disease was significantly higher than that of young and inexperienced doctors.ConclusionDermatoscopy combined with machine learning can effectively improve the diagnosis and differential diagnosis accuracy of rosacea and other facial inflammatory skin diseases.

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).

An Empirical Study on Ensemble of Segmentation Approaches

Recognizing objects in images requires complex skills that involve knowledge about the context and the ability to identify the borders of the objects. In computer vision, this task is called semantic segmentation and it pertains to the classification of each pixel in an image. The task is of main importance in many real-life scenarios: in autonomous vehicles, it allows the identification of objects surrounding the vehicle; in medical diagnosis, it improves the ability of early detecting of dangerous pathologies and thus mitigates the risk of serious consequences. In this work, we propose a new ensemble method able to solve the semantic segmentation task. The model is based on convolutional neural networks (CNNs) and transformers. An ensemble uses many different models whose predictions are aggregated to form the output of the ensemble system. The performance and quality of the ensemble prediction are strongly connected with some factors; one of the most important is the diversity among individual models. In our approach, this is enforced by adopting different loss functions and testing different data augmentations. We developed the proposed method by combining DeepLabV3+, HarDNet-MSEG, and Pyramid Vision Transformers. The developed solution was then assessed through an extensive empirical evaluation in five different scenarios: polyp detection, skin detection, leukocytes recognition, environmental microorganism detection, and butterfly recognition. The model provides state-of-the-art results.

Development of skin health test efficacy modules in vivo

Sunlight contains ultraviolet (UV) light that causes sunburn and makes the skin age faster, leading to more wrinkles as older. The UV light can come from the natural and artificial sources. Moreover, UV light has shorter wavelengths than the visible light. Therefore, people’ eyes can’t see UV, but people’ skin can feel it. In this study, the in vivo skin health test efficacy modules have been established via the detection of skin’s moisture retention (%), skin’s cytokine expression levels, enzymatic expressions in the skin, the expression levels of hyaluronic acid (HA), collagen type I, melanin, and malondialdehyde (MDA) in the skin, and the experimental mice’ skin thickness and lesions via histo-pathologic examination. According to the results, the clinical behavior observation indexes of Institute of Cancer Research (ICR) mice in each group were normal during the experiments. Moreover, all ICR mice were survival until the end of the experiments. The moisture retention (%) of skin in ICR mice in UVB group was significant decrease after D1, D3, and D5 of UVB irradiation compared to the normal control group. Based on the IL-1β, IL-6 and TNF-α analysis expressions, both IL-1β and IL-6 expressions in UVB group were significantly increase than the control group, while there was no significant difference in the TNF-α expression between the groups. ICR mice’ skin enzymatic expressions in each group presented that catalase (CAT) expression and superoxide dismutase (SOD) activity in UVB group were significantly lower than the control group. The MDA expression in UVB group were significantly higher than the control group. The HA and collagen type I expressions in UVB group were significantly lower than the control group. However, the melanin expressions in UVB group and the control groups were not significantly different. The matrix metalloproteinase 2 (MMP-2) expressions in UVB group was significantly higher than the control group. The skin epidermal thickness in UVB group was significantly thicker than the control group. The dermal thickness in two groups was not significantly different. The number of sunburn cells in the derma in UVB group was significantly increase than the control group. The solar elastosis in the derma in two groups was not significantly different. Based on the above results, we have successfully established in vivo skin health test efficacy modules to evaluate the status of skin health. We hope the modules should be provide for the research and development (R&amp;D) of the effective treatment included drugs and therapeutic strategies.

COMPUTER WORD RECOGNITION- IMAGE PROCESSING USING SIGN GESTURES- CREATION OF A NOVEL SIGN LANGUAGE

Sign language is used by deaf and hard hearing people to exchange information between their own community and other people. Computer recognition of Sign language deals from Sign gesture acquisition and continues till text/speech generation. Sign gestures can be classified as static and dynamic. However static gesture recognition is simpler than dynamic gesture recognition but both recognition systems are important to the human community. However, most people do not know the sign language. In this project, we aim to automatically recognize sign language Alphabet / Numbers / Words in image- based methodology using MATLAB. The problem addressed is based on Digital Image Processing using Skin Detection, Image Segmentation, Image Filtering, and cross- correlation method. This system expects to achieve recognizing gestures of ISL (Indian Sign Language) by special people and converting into speech/text. The statistic of the result of the implementation, it is therefore concluded that the method is used for cross-correlation and color segmentation work with hand gesture recognition. The results obtained are applicable and can be implemented in a mobile device smart phone having a frontal camera. The vision of an efficient system to translate sign language to text is quite achievable, but the challenges lie in optimization. Key Words: Computer recognition, Skin Detection, ISL (Indian Sign Language)

A Prospective Multicenter Study to Evaluate the Safety and Efficacy of the Topical Application of MYOWNN™, an Autologous Growth Factor Concentrate (AGFC) Serum, in Anti-Aging.

BackgroundGrowth factors from platelets have been emerging as a revolutionary treatment with the ability to induce cell growth in the skin, which results in retarding and reversing the aging process. Platelet-rich plasma (PRP) allows for greater release of growth factors and biologically active proteins, which in turn activates the cascade of stimulation of neoangiogenesis and collagen production. PRP is used in anti-aging and facial skin rejuvenation in the form of dermal injections and topical application during micro-needling. This study was conducted to assess the safety and efficacy of a topically applied face serum, MYOWNN™ (Wockhardt Ltd., Mumbai, India). MYOWNN™ is an autologous growth factor concentrate that has been made into a topical face serum.MethodsMale and female subjects in the age group between 30 and 55 years (both inclusive) with Fitzpatrick skin type III-V who had not taken any oral or topical treatments for at least four weeks before and any platelet-rich plasma (PRP) based facial treatment (injections) at least six months before the study entry were included. MYOWNN™ serum was applied on the face once daily at night, approximately 30 minutes before sleeping preferably, for a total duration of five months. Six parameters, i.e. spots, pores, wrinkles, texture, moisture, and pigmentation, were evaluated at regular intervals with Visage-LS (dermaindia®, Tamil Nadu, India), a face analysis system that gives the live status of these six parameters and is an advanced live status skin detection equipment together with shooting, analyzing, and displaying functions, as well as the subjective analysis, was performed by subjects and physicians using different globally accepted scales like physician’s global aesthetic improvement scale (PGAIS), subject’s global aesthetic improvement scale (SGAIS), subject satisfaction score (SSS), and wrinkle severity rating scale (WSRS). For analysis, a mixed model for repeated measures was used. The model had change from baseline as the dependent variable visit as a factor and baseline assessment result as a covariate. All primary and secondary efficacy endpoints were analyzed using Modified Intent-to-Treat (mITT) populations.ResultsImprovement in an average of six anti-aging parameters was observed as early as three months while statistically significant improvement was observed by the end of five months of application. A statistically significant improvement in wrinkles was observed by the end of three months of the application itself. There were no product-related adverse events reported.ConclusionsFive months of application of MYOWNN™ serum showed a statistically significant improvement in an average of six parameters of anti-aging and face rejuvenation with a p-value of 0.0150 (<5% level of significance (i.e. 0.05) and was also well-tolerated.

Non-contact automatic vital signs monitoring of neonates in NICU using video camera imaging

ABSTRACT Non-contact vital signs monitoring is desirable in both clinical and non-clinical sectors. However, there are limited camera-based studies to monitor the vital signs of neonatal infants in a clinical setting. In this study, a digital camera was used to remotely monitor the heart rate (HR) and respiratory rate (RR) of seven infants at Flinders Medical Centre Neonatal Intensive Care Unit (NICU). The region-of-interest (ROI) was automatically selected based on a skin detection method using skin hue. The experimental results have been validated with the reference data obtained from an ECG monitor, and they showed a strong correlation using the Pearson correlation coefficient, (PCC) .9836 and .9426 for HR and RR, respectively, and a lower error rate with RMSE 2.66 beats/min and 2.93 breaths/min between measured data and reference data. A Bland Altman analysis of the data also presented a close correlation between measured data and reference data for both HR and RR with a mean bias .69 beats/min and .28 breaths/min, and the lower and upper limit of agreement −4.4 to +5.8 beats/min and −5.5 to +6.1 breaths/min, respectively. Therefore, video camera imaging may replace conventional monitoring in NICU and has potential applications in other contexts such as home health monitoring.

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%.