Novel and Tuneable Method for Skin Detection Based on Hybrid Color Space and Color Statistical Features
Shahid Rajaee Teacher Training University（沙希德·拉贾伊师范大学）
Reza Azad, Hamid Reza Shayegh
备注：
2013 First International Conference on computer, Information Technology and Digital Media. arXiv admin note: substantial text overlap with arXiv:1407.6318
Skin detection is one of the most important and primary stages in some of image processing applications such as face detection and human tracking. So far, many approaches are proposed to done this case. Near all of these methods have tried to find best match intensity distribution with skin pixels based on popular color spaces such as RGB, CMYK or YCbCr. Results show these methods cannot provide an accurate approach for every kinds of skin. In this paper, an approach is proposed to solve this problem using statistical features technique. This approach is including two stages. In the first one, from pure skin statistical features were extracted and at the second stage, the skin pixels are detected using HSV and YCbCr color spaces. In the result part, the proposed approach is applied on FEI database and the accuracy rate reached 99.25 + 0.2. Further proposed method is applied on complex background database and accuracy rate obtained 95.40+0.31%. The proposed approach can be used for all kinds of skin using train stage which is the main advantages of it. Low noise sensitivity and low computational complexity are some of other advantages.
Virginia Tech（弗吉尼亚理工大学）；Virginia Tech Transportation Institute（弗吉尼亚理工交通研究所）
This paper addresses the problem of automatically detecting human skin in images without reliance on color information. A primary motivation of the work has been to achieve results that are consistent across the full range of skin tones, even while using a training dataset that is significantly biased toward lighter skin tones. Previous skin-detection methods have used color cues almost exclusively, and we present a new approach that performs well in the absence of such information. A key aspect of the work is dataset repair through augmentation that is applied strategically during training, w…
Escola Politècnica Superior, Universitat de Girona（赫罗纳大学高等理工学院）
Fully automatic detection of skin lesions in dermatoscopic images can facilitate early diagnosis and repression of malignant melanoma and non-melanoma skin cancer. Although convolutional neural networks are a powerful solution, they are limited by the illumination spectrum of annotated dermatoscopic screening images, where color is an important discriminative feature. In this paper, we propose an adaptive color augmentation technique to amplify data expression and model performance, while regulating color difference and saturation to minimize the risks of using synthetic data. Through deep vis…
Vali-e-Asr University of Rafsanjan（拉夫桑詹瓦利阿斯尔大学）；Ontario Tech University（安大略理工大学）；IASBS（基础科学高级研究所）
Skin color detection is an essential required step in various applications related to computer vision. These applications will include face detection, finding pornographic images in movies and photos, finding ethnicity, age, diagnosis, and so on. Therefore, proposing a proper skin detection method can provide solution to several problems. In this study, first a new color space is created using FCM and PSO algorithms. Then, skin classification has been performed in the new color space utilizing linear and nonlinear modes. Additionally, it has been done in RGB and LAB color spaces by using ANFIS…
While deep learning based approaches have demonstrated expert-level performance in dermatological diagnosis tasks, they have also been shown to exhibit biases toward certain demographic attributes, particularly skin types (e.g., light versus dark), a fairness concern that must be addressed. We propose CIRCLe, a skin color invariant deep representation learning method for improving fairness in skin lesion classification. CIRCLe is trained to classify images by utilizing a regularization loss that encourages images with the same diagnosis but different skin types to have similar latent represent…
From The late 90th, "Skin Detection" becomes one of the major problems in image processing. If "Skin Detection" will be done in high accuracy, it can be used in many cases as face recognition, Human Tracking and etc. Until now so many methods were presented for solving this problem. In most of these methods, color space was used to extract feature vector for classifying pixels, but the most of them have not good accuracy in detecting types of skin. The proposed approach in this paper is based on "Color based image retrieval" (CBIR) technique. In this method, first by means of CBIR method and i…
INESC TEC；Tecnalia（特克纳利亚）；Computer Vision Center, Universitat Autónoma de Barcelona（巴塞罗那自治大学计算机视觉中心）；Faculdade de Engenharia, Universidade do Porto（波尔图大学工程学院）
Dermoscopic skin images are often obtained with different imaging devices, under varying acquisition conditions. In this work, instead of attempting to perform intensity and color normalization, we propose to leverage computational color constancy techniques to build an artificial data augmentation technique suitable for this kind of images. Specifically, we apply the \emph{shades of gray} color constancy technique to color-normalize the entire training set of images, while retaining the estimated illuminants. We then draw one sample from the distribution of training set illuminants and apply …
North South University（北南大学）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
The increasing popularity of animes makes it vulnerable to unwanted usages like copyright violations and pornography. That is why, we need to develop a method to detect and recognize animation characters. Skin detection is one of the most important steps in this way. Though there are some methods to detect human skin color, but those methods do not work properly for anime characters. Anime skin varies greatly from human skin in color, texture, tone and in different kinds of lighting. They also vary greatly among themselves. Moreover, many other things (for example leather, shirt, hair etc.), w…
Human face perception is currently an active research area in the computer vision community. Skin detection is one of the most important and primary stages for this purpose. So far, many approaches are proposed to done this case. Near all of these methods have tried to find best match intensity distribution with skin pixels based on popular color spaces such as RGB, HSI or YCBCR. Results show that these methods cannot provide an accurate approach for every kind of skin. In this paper, an approach is proposed to solve this problem using a color probabilistic estimation technique. This approach …
In this paper, a reliable method for detecting human faces in color images is proposed. This system firstly detects skin color in YCgCr and YIQ color space, then filters binary texture and the result is morphological processed, finally converts skin tone to the preferred skin color configured by users in YIQ color space. The real-time adjusting circuit is implemented and some of simulation results are given out. Experimental results demonstrate that the method has achieved high rates and low false positives, another advantage is its simplicity and minor computational costs.
Thadomal Shahani Engineering College（塔多马尔沙哈尼工程学院）；Sardar Patel Institute of Technology（萨达尔帕特尔理工学院）
Human Skin detection deals with the recognition of skin-colored pixels and regions in a given image. Skin color is often used in human skin detection because it is invariant to orientation and size and is fast to process. A new human skin detection algorithm is proposed in this paper. The three main parameters for recognizing a skin pixel are RGB (Red, Green, Blue), HSV (Hue, Saturation, Value) and YCbCr (Luminance, Chrominance) color models. The objective of proposed algorithm is to improve the recognition of skin pixels in given images. The algorithm not only considers individual ranges of t…
VIT University（VIT大学）；SSN College of Engineering（SSN工程学院）
Skin cancer is the most common of all cancers and each year million cases of skin cancer are treated. Treating and curing skin cancer is easy, if it is diagnosed and treated at an early stage. In this work we propose an automatic technique for skin lesion segmentation in dermoscopic images which helps in classifying the skin cancer types. The proposed method comprises of two major phases (1) preprocessing and (2) segmentation using semi-supervised learning algorithm. In the preprocessing phase noise are removed using filtering technique and in the segmentation phase skin lesions are segmented …
Sarhad University of Science and Information Technology（萨哈德科技与信息技术大学）；RWTH Aachen（亚琛工业大学）；UET Mardan（马尔丹工程大学）
On dedicated websites, people can upload videos and share it with the rest of the world. Currently these videos are cat- egorized manually by the help of the user community. In this paper, we propose a combination of color spaces with the Bayesian network approach for robust detection of skin color followed by an automated video categorization. Exper- imental results show that our method can achieve satisfactory performance for categorizing videos based on skin color.
Northeastern University（东北大学）；Beihang University（北京航空航天大学）；Microsoft Research Asia（微软亚洲研究院）；Meidaojia（美道家）
Modern approaches for semantic segmention usually pay too much attention to the accuracy of the model, and therefore it is strongly recommended to introduce cumbersome backbones, which brings heavy computation burden and memory footprint. To alleviate this problem, we propose an efficient segmentation method based on deep convolutional neural networks (DCNNs) for the task of hair and facial skin segmentation, which achieving remarkable trade-off between speed and performance on three benchmark datasets. As far as we know, the accuracy of skin tones classification is usually unsatisfactory due …
This study aims to investigate implementing EM and FCM algorithms for skin color extraction. The capabilities of three well-known color spaces, namely, RGB, HSV, and YCbCr for skin-tone extraction are assessed by using statistical modeling of skin tones using EM and FCM algorithms. The results show that utilizing a Gaussian mixture model for parametric modeling of skin tones using EM algorithm works well in HSV color space when all three components of the color vector are used. In spite of discarding the luminance components in YCbCr and HSV color spaces, EM algorithm provides the best results…
Skin Segmentation is widely used in biometric applications such as face detection, face recognition, face tracking, and hand gesture recognition. However, several challenges such as nonlinear illumination, equipment effects, personal interferences, ethnicity variations, etc., are involved in detection process that result in the inefficiency of color based methods. Even though many ideas have already been proposed, the problem has not been satisfactorily solved yet. This paper introduces a technique that addresses some limitations of the previous works. The proposed algorithm consists of three …
Shinshu University（信州大学）；The University of Kitakyushu（北九州市立大学）；Tokyo Institute of Technology（东京技术大学）；Doshisha University（立命馆大学）；The University Institute of Technology La Rochelle（拉罗什大学技术学院）
本文提出了一种自动图像校正方法，用于人像照片，通过抑制背景颜色引起的肤色变化来提高面部肤色的一致性。在人像摄影中，由于光照环境（如从彩色背景墙反射的光线或相机 strobe 过曝）常导致肤色失真，若照片人工合成其他背景色，则这种颜色变化会更加明显，导致不自然的合成结果。在我们的框架中，首先大致提取面部区域并在颜色空间中校正肤色分布，然后在原始图像中对面部周围进行颜色和亮度校正，以实现适当的面部颜色平衡，不受亮度和背景颜色影响。与传统颜色校正算法不同，我们的最终结果通过带有引导图像的颜色校正过程获得。特别是，我们的引导图像过滤器在颜色校正中不需要像 He 等人最初提出的引导图像过滤器方法中所需的完美对齐的引导图像。实验结果表明，我们的方法在人像照片和自然场景照片上都比传统方法生成更自然的结果。我们还展示了自动年鉴风格照片生成作为另一种应用。
Automatic assessment and understanding of facial skin condition have several applications, including the early detection of underlying health problems, lifestyle and dietary treatment, skin-care product recommendation, etc. Selfies in the wild serve as an excellent data resource to democratize skin quality assessment, but suffer from several data collection challenges.The key to guaranteeing an accurate assessment is accurate detection of different skin features. We present an automatic facial skin feature detection method that works across a variety of skin tones and age groups for selfies in…
Institute of Information Technology, University of Dhaka（达卡大学信息技术学院）；Department of Statistics, Biostatistics and Informatics, University of Dhaka（达卡大学统计、生物统计与信息学系）
Object detection has been a focus of research in human-computer interaction. Skin area detection has been a key to different recognitions like face recognition, human motion detection, pornographic and nude image prediction, etc. Most of the research done in the fields of skin detection has been trained and tested on human images of African, Mongolian and Anglo-Saxon ethnic origins. Although there are several intensity invariant approaches to skin detection, the skin color of Indian sub-continentals have not been focused separately. The approach of this research is to make a comparative study …
Shanghai Institute of Applied Physics, Chinese Academy of Sciences（中国科学院上海应用物理研究所）
A novel and uniform framework for face verification is presented in this paper. First of all, a 2-directional 2-dimensional feature extraction method is adopted to extract client-specific template - 2D discrimant projection matrix. Then the face skin color information is utilized as an additive feature to enhance decision making strategy that makes use of not only 2D grey feature but also 2D skin color feature. A fusion decision of both is applied to experiment the performance on the XM2VTS database according to Lausanne protocol. Experimental results show that the framework achieves high veri…
Exploring the Impact of Skin Color on Skin Lesion Segmentation
School of Digital and Physical Sciences at the University of Hull（赫尔大学数字与物理科学学院）；Faculty of Electrical Engineering at the University of Sarajevo（萨拉热窝大学电气工程学院）
Department of CS & IT, SUIT Peshawar（白沙瓦SUIT计算机科学与信息技术系）
Skin lesion segmentation can be rather a challenging task owing to the presence of artifacts, low contrast between lesion and boundary, color variegation, fuzzy skin lesion borders and heterogeneous background in dermoscopy images. In this paper, we propose a simple yet effective saliency-based approach derived in the frequency and spatial domain to detect pigmented skin lesion. Two color models are utilized for the construction of these maps. We suggest a different metric for each color model to design map in the spatial domain via color features. The map in the frequency domain is generated …
University of Burgundy（勃艮第大学）；University of Cassino（卡西诺大学）；University of Girona（赫罗纳大学）
Skin lesion segmentation is one of the first steps towards automatic Computer-Aided Diagnosis of skin cancer. Vast variety in the appearance of the skin lesion makes this task very challenging. The contribution of this paper is to apply a power foreground extraction technique called GrabCut for automatic skin lesion segmentation with minimal human interaction in HSV color space. Preprocessing was performed for removing the outer black border. Jaccard Index was measured to evaluate the performance of the segmentation method. On average, 0.71 Jaccard Index was achieved on 1000 images from ISIC c…
Face detection is one of the challenging tasks in computer vision. Human face detection plays an essential role in the first stage of face processing applications such as face recognition, face tracking, image database management, etc. In these applications, face objects often come from an inconsequential part of images that contain variations, namely different illumination, poses, and occlusion. These variations can decrease face detection rate noticeably. Most existing face detection approaches are not accurate, as they have not been able to resolve unstructured images due to large appearanc…
Chittagong University of Engineering and Technology（吉大港工程技术大学）
In our current web and mobile application development runtime nude image content detection is very important. This paper presents a runtime nudity detection method for web and mobile application development. We use two parameters to detect the nude content of an image. One is the number of skin pixels another is face region. A skin color model based on RGB, HSV color spaces are used to detect skin pixels in an image. Google vision api is used to detect the face region. By the percentage of skin regions and face regions an image is identified nude or not. The success of this algorithm exists in…
Human skin detection in images is a widely studied topic of Computer Vision for which it is commonly accepted that analysis of pixel color or local patches may suffice. This is because skin regions appear to be relatively uniform and many argue that there is a small chromatic variation among different samples. However, we found that there are strong biases in the datasets commonly used to train or tune skin detection methods. Furthermore, the lack of contextual information may hinder the performance of local approaches. In this paper we present a comprehensive evaluation of holistic and local …
National Research Council, Institute for the Application of Calculus（国家研究委员会，计算应用研究所）
Skin lesion segmentation is one of the crucial steps for an efficient non-invasive computer-aided early diagnosis of melanoma. This paper investigates how color information, besides saliency, can be used to determine the pigmented lesion region automatically. Unlike most existing segmentation methods using only the saliency in order to discriminate against the skin lesion from the surrounding regions, we propose a novel method employing a binarization process coupled with new perceptual criteria, inspired by the human visual perception, related to the properties of saliency and color of the in…
DISI, Università di Bologna（博洛尼亚大学DISI学院）；DEI - University of Padova（帕多瓦大学DEI学院）
Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In this work, we investigate the most recent researches in this field and we propose a fair comparison among approaches using several different d…
Pattern recognition problem rely upon the features inherent in the pattern of images. Face detection and recognition is one of the challenging research areas in the field of computer vision. In this paper, we present a method to identify skin pixels from still and video images using skin color. Face regions are identified from this skin pixel region. Facial features such as eyes, nose and mouth are then located. Faces are recognized from color images using an RBF based neural network. Unsupervised Cellular Automata with K means clustering algorithm is used to locate different facial elements. …
Person re-identification in a multi-camera environment is an important part of modern surveillance systems. Person re-identification from color images has been the focus of much active research, due to the numerous challenges posed with such analysis tasks, such as variations in illumination, pose and viewpoints. In this paper, we suggest that hyperspectral imagery has the potential to provide unique information that is expected to be beneficial for the re-identification task. Specifically, we assert that by accurately characterizing the unique spectral signature for each person's skin, hypers…
Malignant melanoma has one of the most rapidly increasing incidences in the world and has a considerable mortality rate. Early diagnosis is particularly important since melanoma can be cured with prompt excision. Dermoscopy images play an important role in the non-invasive early detection of melanoma [1]. However, melanoma detection using human vision alone can be subjective, inaccurate and poorly reproducible even among experienced dermatologists. This is attributed to the challenges in interpreting images with diverse characteristics including lesions of varying sizes and shapes, lesions tha…
With the increasing popularity of augmented and virtual reality, retailers are now focusing more towards customer satisfaction to increase the amount of sales. Although augmented reality is not a new concept but it has gained much needed attention over the past few years. Our present work is targeted towards this direction which may be used to enhance user experience in various virtual and augmented reality based applications. We propose a model to change skin tone of a person. Given any input image of a person or a group of persons with some value indicating the desired change of skin color t…
In this paper, an efficient skin detection system is proposed. The algorithm is based on a very fast efficient pre-processing step utilizing the concept of ternary conversion in order to identify candidate windows and subsequently, a novel local two-stage diffusion method which has F-score accuracy of 0.5978 on SDD dataset. The pre-processing step has been proven to be useful to boost the speed of the system by eliminating 82% of an image in average. This is obtained by keeping the true positive rate above 98%. In addition, a novel segmentation algorithm is also designed to process candidate w…
A Multi-Task Deep Learning Framework for Skin Lesion Classification, ABCDE Feature Quantification, and Evolution Simulation
Department of Chemistry, University of Cambridge, Cambridge, United Kingdom（剑桥大学化学系，剑桥，英国）；Harvard Ophthalmology AI Lab, Harvard Medical School, Boston, MA, USA（哈佛医学院眼科人工智能实验室，哈佛医学院，波士顿，马萨诸塞州，美国）
Tlemcen University（特莱姆森大学）；Université de Bourgogne（勃艮第大学）
During the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer. In this work, we present an automated diagnosis system based on the ABCD rule used in clinical diagnosis in order to discriminate benign from malignant skin lesions. First, to reduce the influence of small structures, a preprocessing step based on morphological and fast marching schemes is used. In the second step, an unsupervised approach for lesion se…
UC San Diego（加州大学圣地亚哥分校）；NovuMind Inc.（诺维迈德公司）；Hewlett-Packard, Inc.（惠普公司）
Hand segmentation for hand-object interaction is a necessary preprocessing step in many applications such as augmented reality, medical application, and human-robot interaction. However, typical methods are based on color information which is not robust to objects with skin color, skin pigment difference, and light condition variations. Thus, we propose hand segmentation method for hand-object interaction using only a depth map. It is challenging because of the small depth difference between a hand and objects during an interaction. To overcome this challenge, we propose the two-stage random d…
Manchester Metropolitan University（曼彻斯特城市大学）；University of Sheffield（谢菲尔德大学）
Lesion diagnosis of skin lesions is a very challenging task due to high inter-class similarities and intra-class variations in terms of color, size, site and appearance among different skin lesions. With the emergence of computer vision especially deep learning algorithms, lesion diagnosis is made possible using these algorithms trained on dermoscopic images. Usually, deep classification networks are used for the lesion diagnosis to determine different types of skin lesions. In this work, we used pixel-wise classification network to provide lesion diagnosis rather than classification network. …
We utilize the dynamics involved in the imaging of a fingerprint on a touch-based fingerprint reader, such as perspiration, changes in skin color (blanching), and skin distortion, to differentiate real fingers from spoof (fake) fingers. Specifically, we utilize a deep learning-based architecture (CNN-LSTM) trained end-to-end using sequences of minutiae-centered local patches extracted from ten color frames captured on a COTS fingerprint reader. A time-distributed CNN (MobileNet-v1) extracts spatial features from each local patch, while a bi-directional LSTM layer learns the temporal relationsh…
LSSF-Net: Lightweight Segmentation with Self-Awareness, Spatial Attention, and Focal Modulation
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences & Technology (NUST)（国家科学技术大学电气与计算机科学学院）；Department of Electrical Engineering, Abasyn University Islamabad Campus（阿巴辛大学伊斯兰堡校区电气工程系）；School of Computer Science and Engineering, UNSW（新南威尔士大学计算机科学与工程学院）
Background: Skin cancer is one of the widely seen cancer worldwide and automatic classification of skin cancer can be benefited dermatology clinics for an accurate diagnosis. Hence, a machine learning-based automatic skin cancer detection model must be developed. Material and Method: This research interests to overcome automatic skin cancer detection problem. A colored skin cancer image dataset is used. This dataset contains 3297 images with two classes. An automatic multilevel textural and deep features-based model is presented. Multilevel fuse feature generation using discrete wavelet transf…
This paper presents face recognition using maximum a posteriori (MAP) discriminant on YCbCr color space. The YCbCr color space is considered in order to cover the skin information of face image on the recognition process. The proposed method is employed to improve the recognition rate and equal error rate (EER) of the gray scale based face recognition. In this case, the face features vector consisting of small part of dominant frequency elements which is extracted by non-blocking DCT is implemented as dimensional reduction of the raw face images. The matching process between the query face fea…
An Image Dataset of Common Skin Diseases of Bangladesh and Benchmarking Performance with Machine Learning Models
Department of Computer Science and Engineering, Faridpur Engineering College, Faridpur, Bangladesh（孟加拉国法里德普尔工程学院计算机科学与工程系）；Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh（孟加拉国达卡大学计算机科学与工程系）；Department of Oncology, Labaid Cancer and Super Speciality Center, Dhaka, Bangladesh（孟加拉国达卡拉贝德癌症与超级专科中心肿瘤科）；Institute of Information Technology, University of Dhaka, Dhaka, Bangladesh（孟加拉国达卡大学信息技术研究所）
While the LED panels used in virtual production systems can display vibrant imagery with a wide color gamut, they produce problematic color shifts when used as lighting due to their peaky spectral output from narrow-band red, green, and blue LEDs. In this work, we present an improved color calibration process for virtual production stages which ameliorates this color rendition problem while also passing through accurate in-camera background colors. We do this by optimizing linear color correction transformations for 1) the LED panel pixels visible in the field of view of the camera, 2) the pix…
A Wavelet Guided Attention Module for Skin Cancer Classification with Gradient-based Feature Fusion
Jadavpur University（jadavpur大学）；International Institute of Information Technology（国际信息技术学院）；China University of Mining and Technology（中国矿业大学）；Saint Petersburg Electrotechnical University "LETI"（圣彼得堡电工技术大学“LETI”）
