Skin Region Detection Research Articles

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.

Skin detection in video under uncontrolled illumination

Many vision-based human-computer interaction (HCI) applications require skin detection. However, their performance relies on accuracy in detecting skin regions in video, which is difficult under uncontrolled illumination. The chromatic appearance of skin changes because of shading, often caused by body movement. To address this, we propose a dynamic adaptation method to detect skin regions affected by local color deformations. Static and dynamic skin regions are detected by a corresponding module. The static module includes a facial skin distribution model (FSDM) and a fusion-based background distribution model (FBDM). The FBDM is obtained from a local background distribution model (LBDM) and a global background distribution model (GBDM). The LBDM is obtained by comparing a frame pixel distribution model with the FSDM and GBDM. Next, the FBDM is derived from the LBDM and the GBDM. The dynamic module includes a moving skin distribution model (MSDM), derived from a set of moving skin samples. Initially, moving skin regions are detected using a modified double frame-difference method and then modeled using a Gaussian mixture model. To avoid misidentifying background regions as skin, the final MSDM is obtained by comparing the initial moving skin model to the FSDM and FBDM. Finally, the static and the dynamic models are fused by applying a maximization rule. Experimental results shows that the proposed method can detect skin regions more accurately than state-of-the-art methods.

Effective detection of exposed target regions based on deep learning from multimedia data

With the development of high-performance visual sensors, it has been very easy to obtain a variety of image data. Of these image data, human face regions contain personal information to distinguish one from the others. Therefore, it is important to accurately detect unhidden face regions from an input image. This paper proposes a method of robustly detecting human face regions from an input color image with the use of a deep learning algorithm, one of the machine learning algorithms. The proposed method first transforms the RGB color model of an input image to the YCbCr color model, and then removes other regions than face regions to segment skin regions with the use of the pre-learned elliptical skin color distribution model. Subsequently, a CNN model-based deep learning algorithm was applied to robustly detect human face regions from the detected skin regions in the previous step. As a result, the proposed method segments face regions more efficiently than an existing method. The face region detection method proposed in this paper is expected to be usefully applied to practical areas related to multimedia data processing, such as video surveillance, target blocking, image security, visual data analysis, and object recognition and tracking.

Monitoring System for Persons With Alzheimer's Disease via Video-Object Tracking

This article presents a framework for a food intake monitoring system intended for use with persons with Alzheimer's disease and other dementias. Alzheimer's disease has a significant impact on the individual's ability to perform their daily activities including eating. Providing assistance with feeding is a major challenge for caregivers, including a significant time commitment. We present a vision-based system that tracks moving objects, such as the hand, using a combined optical flow and skin region detection algorithms. Skin detection is implemented using two different methods. Hue, saturation, and value (HSV) color space, which is on separation of the illuminance component from chrominance one as the first method and skin color information is extracted from subject's face detected using Viola-Johns algorithm for the second method. Once face and other moving skin regions are detected, bounding boxes are created and used to track all moving regions over the video frames, recognizing eating behavior or the lack of it. Based on experimental results the proposed method using optical flow and skin regions segmentation using HSV color detects the hand to mouth eating motion with 92.12% accuracy. The optical flow and skin region segmentation based on face color information achieves a higher accuracy of 94.29%.

Efficient 3D point clouds classification for face detection using linear programming and data mining

ABSTRACTMost of the applications related to security and biometric rely on skin region detection such as face detection, adult 3D objects filtering, and gesture recognition. In this paper, we propose a robust method for skin detection on 3D coloured point clouds. Then, we extend this method to solve the problem of 3D face detection. To do so, we construct a weighted graph from initial coloured 3D point clouds. Then, we present a linear programming algorithm using a predictive model based on a data mining approach to classify and label graph vertices as skin and non-skin regions. Moreover, we apply some refinement rules on skin regions to confirm the presence of a face. Furthermore, we demonstrate the robustness of our method by showing and analysing some experimental results. Finally, we show that our method deals with many data that can be represented by a weighted graph such as 2D images and 3D models.

Skin color model adaptation under varying lighting conditions

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

Color-based skin segmentation in videos using a multi-step spatial method

Recently, skin detection has been employed in multifarious applications of computer vision including face detection, gesture recognition, etc. This is mainly due to the appealing characteristics of skin color and its potency to segment objects. However, there are certain challenges involved in utilizing human complexion as a feature to detect faces, and they have led to the inefficiency of many methods. In order to counteract these factors, in this paper, a skin segmentation method which exploits a multi step diffusion algorithm to detect skin regions is presented. The method starts with conservative extraction of skin seeds in each frame which is accomplished by using fusion of ternary-based human motion detection, modified Bayesian classifier, and a feedback mechanism. Subsequently, these candidate skin pixels are utilized in a 2-stage diffusion scheme to detect other skin pixels. Both quantitative and qualitative results demonstrate the effectiveness of the proposed system in comparison with other works.

Unsupervised Subject Detection via Remote PPG.

Subject detection is a crucial task for camera-based remote healthcare monitoring. Most existing methods in subject detection rely on supervised learning of physical appearance features. However, their performances are highly restricted to the pretrained appearance model, while still suffering from the false detection of human-similar objects. In this paper, we propose a novel unsupervised method to detect alive subject in a video using physiological features. Our basic idea originates from the observation that only living skin tissue of a human presents pulse signals, which can be exploited as the feature to distinguish human skin from nonhuman surfaces in videos. The proposed VPS method, named voxel-pulse-spectral, consists of three steps: it 1) creates hierarchical voxels across the video for temporally parallel pulse extraction; 2) builds a similarity matrix for hierarchical pulse signals based on their intrinsic properties; and 3) utilizes incremental sparse matrix decomposition with hierarchical fusion to robustly identify and combine the voxels that correspond to single/multiple subjects. Numerous experiments demonstrate the superior performance of VPS over a state-of-the-art method. On average, VPS improves 82.2% on the precision of skin-region detection; 595.5% on the Pearson correlation, and 542.2% on Bland-Altman agreement of instant pulse rate. ANOVA shows that in all-round evaluations, the improvements of VPS are significant. The proposed method is the first method that uses pulse to robustly detect alive subjects in realistic scenarios, which can be favorably applied for healthcare monitoring.

Preferred Racial Skin Color Reproduction in an Image based on Race Classification

Preferred skin color reproduction is an important factor for improving the image quality of display devices and systems. Therefore, this article presents a race-selective preferred skin color reproduction method for the case of multiple races in a single image. First, the proposed method detects skin regions using a focus map and skin candidate map. Next, the race of each detected skin region is classified by comparing the Euclidean distance of the average chromaticity for each detected skin region with a database of racial skin colors. Based on this classification, the racial preferred skin color for each detected skin region is then selected from candidate racial preferred skin colors that are predefined in a database generated through an observer’s preference test. Finally, the skin color is reproduced toward the preferred skin color. Experimental results confirm that the proposed method can achieve race-selective preferred skin color reproduction in the case of multiple races in a single image.

경사도 맵과 채도 특징을 이용한 적응적 피부영역 검출에 관한 연구

Real-time body detection has been researched actively. On the other hand, the detection rate of color distorted images is low because most existing detection methods use static skin color model. Therefore, this paper proposes a new method for detecting the skin color region using a gradient map and saturation features. The basic procedure of the proposed method sequentially consists of creating a gradient map, extracting a gradient feature of skin regions, noise removal using the saturation features of skin, creating a cluster for extraction regions, detecting skin regions using cluster information, and verifying the results. This method uses features other than the color to strengthen skin detection not affected by light, race, age, individual features, etc. The results of the detection rate showed that the proposed method is 10% or more higher than the traditional methods.

Skin Segmentation Using YUV and RGB Color Spaces

Skin detection is used in many applications, such as face recognition, hand tracking, and human-computer interaction. There are many skin color detection algorithms that are used to extract human skin color regions that are based on the thresholding technique since it is simple and fast for computation. The efficiency of each color space depends on its robustness to the change in lighting and the ability to distinguish skin color pixels in images that have a complex background. For more accurate skin detection, we are proposing a new threshold based on RGB and YUV color spaces. The proposed approach starts by converting the RGB color space to the YUV color model. Then it separates the Y channel, which represents the intensity of the color model from the U and V channels to eliminate the effects of luminance. After that the threshold values are selected based on the testing of the boundary of skin colors with the help of the color histogram. Finally, the threshold was applied to the input image to extract skin parts. The detected skin regions were quantitatively compared to the actual skin parts in the input images to measure the accuracy and to compare the results of our threshold to the results of other's thresholds to prove the efficiency of our approach. The results of the experiment show that the proposed threshold is more robust in terms of dealing with the complex background and light conditions than others.

Performance and Analysis of Face Recognition Using Skin Color Segmentation Algorithm

Face Recognition System has been an important and active research field because it offers many applications, especially in video surveillance, biometrics, or video coding. The goal of this project is to detect a human's face. The face detection algorithm involved color-based skin segmentation and image filtering. The face location is determined by calculating the centroid of the detected region. However, it is difficult to develop a complete robust face detector due to various light conditions, face sizes, face orientations, background and skin colors. This detects skin regions over the entire image, and then generates face candidates based on a connected component analysis then the face candidates are divided into human face and non-face images.

A Shot Boundary Detection Method for News Video based Human Skin Region (Face) Detection

A New simple approach to detect, classify shot boundaries and store shot boundary frames in Video sequence using human skin region detection based approach is proposed. Human skin region detection is the process of detecting skin region in sequence of frames. Skin region detection is mainly used for the identification of the human face detection. This approach is very much suitable for finding shots in TV News so that we can classify anchor and non-anchor frames to save the overall time which is required to watch overall news,

Mixture models with skin and shadow probabilities for fingertip input applications

This paper proposes an accurate moving skin region detection method for video-based human–computer interface using gestures or fingertips. Using Gaussian mixture models as groundwork, the proposed method expresses the features of skins in a probability form and incorporates them into the mixture-based framework. Moreover, to alleviate the influence of shadows, the properties of shadows are also formulated as probabilities and used for shadow detection and elimination. In addition to moving skin region detection, this paper also develops two practical fingertip input applications to demonstrate the accuracy of the proposed detection method. The two applications are Mandarin Phonetic Symbol combination recognition system and single fingertip virtual keyboard implementation. Experimental results have shown the advantages of the proposed detection method and the effectiveness of the two application implementations.

Skin Region Detection Using a Mean Shift Algorithm Based on the Histogram Approximation

In conventional, skin detection methods using for skin color definitions is based on prior knowledge. By experimentation, the threshold value for dividing the background from the skin region is determined subjectively. A drawback of such techniques is that their performance is dependent on a threshold value which is estimated from repeated experiments. To overcome this, the present paper introduces a skin region detection method. This method uses a histogram approximation based on the mean shift algorithm. This proposed method applies the mean shift procedure to a histogram of a skin map of the input image. It is generated by comparing with the standard skin colors in the <TEX>$C_bC_r$</TEX> color space. It divides the background from the skin region by selecting the maximum value according to the brightness level. As the histogram has the form of a discontinuous function. It is accumulated according to the brightness values of the pixels. It is then, approximated by a Gaussian mixture model (GMM) using the Bezier curve technique. Thus, the proposed method detects the skin region using the mean shift procedure to determine a maximum value. Rather than using a manually selected threshold value, as in existing techniques this becomes the dividing point. Experiments confirm that the new procedure effectively detects the skin region.

A human-machine cooperative system for generating sign language animation using thermal image processing, a fuzzy algorithm, and simulated annealing

We propose a method for a sign language animation by skin region detection applied to an infrared thermal image. In a system incorporating the proposed method, a 3D CG model corresponding to a person's characteristic posture while using sign language is generated automatically by pattern recognition of the thermal image, and then a person's hand in the CG model is set. The hand part is made manually beforehand. If necessary, the model can be replaced manually by a more appropriate model corresponding to training key frames, and/or the same generated model can be refined manually. In our experiments, three hearing-impaired people, who were experienced in using sign language, recognized the Japanese sign language gestures of 70 words expressed as animations with 94.3% accuracy. We further improved the system by correcting the position and direction of the hand of the automatically generated model through the use of a fuzzy algorithm and simulated annealing.

Performance Measure of Human Skin Region Detection Based on Hybrid Particle Swarm Optimization

Performance Measure of Human Skin Region Detection Based on Hybrid Particle Swarm Optimization

A HYBRID APPROACH TO HUMAN SKIN REGION DETECTION

Face recognition is important in research areas like machine vision and complex security systems. Skin region detection is a vital factor for processing in such systems. Hence the proposed paper focuses on isolating the regions of an image corresponding to human skin region through the hybrid method. This paper intends to combine the skin region detected from RGB and YCbCr color spaces image by the explicit skin color conditions and the skin label cluster identified from CIELab color space image, which is clustered by Hillclimbing segmentation with K-Means clustering algorithm. Then the resultant image is dilated by arbitrary shape and filtered by the median filter, in order to enhance the skin region and to avoid the noise respectively. The proposed method has been tested on various real images, which contain one or more human beings and the performance of skin region detection is found to be quite satisfactory.

Performance Analysis of Human Skin Region Detection Techniques with Face Detection Application

Performance Analysis of Human Skin Region Detection Techniques with Face Detection Application

A new method for face detection in colour images for emotional bio-robots

Emotional bio-robots have become a hot research topic in last two decades. Though there have been some progress in research, design and development of various emotional bio-robots, few of them can be used in practical applications. The study of emotional bio-robots demands multi-disciplinary co-operation. It involves computer science, artificial intelligence, 3D computation, engineering system modelling, analysis and simulation, bionics engineering, automatic control, image processing and pattern recognition etc. Among them, face detection belongs to image processing and pattern recognition. An emotional robot must have the ability to recognize various objects, particularly, it is very important for a bio-robot to be able to recognize human faces from an image. In this paper, a face detection method is proposed for identifying any human faces in colour images using human skin model and eye detection method. Firstly, this method can be used to detect skin regions from the input colour image after normalizing its luminance. Then, all face candidates are identified using an eye detection method. Comparing with existing algorithms, this method only relies on the colour and geometrical data of human face rather than using training datasets. From experimental results, it is shown that this method is effective and fast and it can be applied to the development of an emotional bio-robot with further improvements of its speed and accuracy.