Human Skin Detection in Color Images Using Deep Learning

Human skin detection is an important and challenging problem in computer vision. Skin detection can be used as the first phase in face detection when using color images. The differences in illumination and ranges of skin colors have made skin detection a challenging task. Gaussian model, rule based methods, and artificial neural networks are methods that have been used for human skin color detection. Deep learning methods are new techniques in learning that have shown improved classification power compared to neural networks. In this paper the authors use deep learning methods in order to enhance the capabilities of skin detection algorithms. Several experiments have been performed using auto encoders and different color spaces. The proposed technique is evaluated compare with other available methods in this domain using two color image databases. The results show that skin detection utilizing deep learning has better results compared to other methods such as rule-based, Gaussian model and feed forward neural network.

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Human Skin Color Detection Using Neural Networks

Journal of Intelligent Systems ◽

10.1515/jisys-2014-0098 ◽

2015 ◽

Vol 24 (4) ◽

pp. 425-436 ◽

Cited By ~ 2

Author(s):

Mohammadreza Hajiarbabi ◽

Arvin Agah

Keyword(s):

Neural Network ◽

Neural Networks ◽

Human Skin ◽

Skin Color ◽

Skin Detection ◽

Color Spaces ◽

Skin Color Detection ◽

The Neural Network ◽

Color Detection ◽

Different Color

AbstractHuman skin detection is an essential phase in face detection and face recognition when using color images. Skin detection is very challenging because of the differences in illumination, differences in photos taken using an assortment of cameras with their own characteristics, range of skin colors due to different ethnicities, and other variations. Numerous methods have been used for human skin color detection, including the Gaussian model, rule-based methods, and artificial neural networks. In this article, we introduce a novel technique of using the neural network to enhance the capabilities of skin detection. Several different entities were used as inputs of a neural network, and the pros and cons of different color spaces are discussed. Also, a vector was used as the input to the neural network that contains information from three different color spaces. The comparison of the proposed technique with existing methods in this domain illustrates the effectiveness and accuracy of the proposed approach. Tests were done on two databases, and the results show that the neural network has better precision and accuracy rate, as well as comparable recall and specificity, compared with other methods.

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Techniques for Skin, Face, Eye and Lip Detection using Skin Segmentation in Color Images

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2015070103 ◽

2015 ◽

Vol 5 (2) ◽

pp. 35-57 ◽

Cited By ~ 2

Author(s):

Mohammadreza Hajiarbabi ◽

Arvin Agah

Keyword(s):

Neural Networks ◽

Human Skin ◽

Face Detection ◽

Skin Color ◽

Color Images ◽

Detection Methods ◽

Skin Segmentation ◽

The Face ◽

Different Color ◽

Lip Detection

Face detection is a challenging and important problem in Computer Vision. In most of the face recognition systems, face detection is used in order to locate the faces in the images. There are different methods for detecting faces in images. One of these methods is to try to find faces in the part of the image that contains human skin. This can be done by using the information of human skin color. Skin detection can be challenging due to factors such as the differences in illumination, different cameras, ranges of skin colors due to different ethnicities, and other variations. Neural networks have been used for detecting human skin. Different methods have been applied to neural networks in order to increase the detection rate of the human skin. The resulting image is then used in the detection phase. The resulting image consists of several components and in the face detection phase, the faces are found by just searching those components. If the components consist of just faces, then the faces can be detected using correlation. Eye and lip detections have also been investigated using different methods, using information from different color spaces. The speed of face detection methods using color images is compared with other face detection methods.

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Novel Techniques in Skin and Face Detection in Color Images

Advancements in Computer Vision and Image Processing - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-5628-2.ch008 ◽

2018 ◽

pp. 190-220

Author(s):

Mohammadreza Hajiarbabi ◽

Arvin Agah

Keyword(s):

Deep Learning ◽

Human Skin ◽

Face Detection ◽

Gaussian Model ◽

Skin Detection ◽

Color Spaces ◽

Feed Forward Neural Network ◽

Black And White ◽

The Face ◽

Different Color

Human skin detection and face detection are important and challenging problems in computer vision. The use of color information has increased in recent years due to the lower processing time of face detection compared to black and white images. A number of techniques for skin detection are discussed. Experiments have been performed utilizing deep learning with a variety of color spaces, showing that deep learning produces better results compared to methods such as rule-based, Gaussian model, and feed forward neural network on skin detection. A challenging problem in skin detection is that there are numerous objects with colors similar to that of the human skin. A texture segmentation method has been designed to distinguish between the human skin and objects with similar colors to that of human skin. Once the skin is detected, image is divided into several skin components and the process of detecting the face is limited to these components—increasing the speed of the face detection. In addition, a method for eye and lip detection is proposed using information from different color spaces.

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Computational Complexity Reduction of Neural Networks of Brain Tumor Image Segmentation by Introducing Fermi–Dirac Correction Functions

Entropy ◽

10.3390/e23020223 ◽

2021 ◽

Vol 23 (2) ◽

pp. 223

Author(s):

Yen-Ling Tai ◽

Shin-Jhe Huang ◽

Chien-Chang Chen ◽

Henry Horng-Shing Lu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Computational Complexity ◽

High Performance ◽

Low Cost ◽

Structural Complexity ◽

Correction Function ◽

Computational Time ◽

Learning Methods ◽

Band Theory

Nowadays, deep learning methods with high structural complexity and flexibility inevitably lean on the computational capability of the hardware. A platform with high-performance GPUs and large amounts of memory could support neural networks having large numbers of layers and kernels. However, naively pursuing high-cost hardware would probably drag the technical development of deep learning methods. In the article, we thus establish a new preprocessing method to reduce the computational complexity of the neural networks. Inspired by the band theory of solids in physics, we map the image space into a noninteraction physical system isomorphically and then treat image voxels as particle-like clusters. Then, we reconstruct the Fermi–Dirac distribution to be a correction function for the normalization of the voxel intensity and as a filter of insignificant cluster components. The filtered clusters at the circumstance can delineate the morphological heterogeneity of the image voxels. We used the BraTS 2019 datasets and the dimensional fusion U-net for the algorithmic validation, and the proposed Fermi–Dirac correction function exhibited comparable performance to other employed preprocessing methods. By comparing to the conventional z-score normalization function and the Gamma correction function, the proposed algorithm can save at least 38% of computational time cost under a low-cost hardware architecture. Even though the correction function of global histogram equalization has the lowest computational time among the employed correction functions, the proposed Fermi–Dirac correction function exhibits better capabilities of image augmentation and segmentation.

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

Multimedia Tools and Applications ◽

10.1007/s11042-021-10955-4 ◽

2021 ◽

Author(s):

Grace L. Samson ◽

Joan Lu

Keyword(s):

Spatial Structure ◽

Human Skin ◽

Skin Color ◽

Skin Detection ◽

Quad Tree ◽

Learning Tasks ◽

Color Detection ◽

Speed Up ◽

Image Pixels ◽

Speed Accuracy

AbstractWe 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.

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An Innovative Face Detection Based on YCgCr Color Space

10.31227/osf.io/x3syk ◽

2018 ◽

Author(s):

Solly Aryza

Keyword(s):

Face Detection ◽

Skin Color ◽

Color Image ◽

Color Space ◽

Gaussian Model ◽

Detection Rates ◽

Detection Algorithms ◽

Lighting Conditions ◽

Wide Range ◽

Human Faces

It is very challenging to recognize a face from an image due to the wide variety of face and the uncertain of face position. The research on detecting human faces in color image and in video sequence has been attracted with more and more people. In this paper, we propose a novel face detection method that achieves better detection rates. The new face detection algorithms based on skin color model in YCgCr chrominance space. Firstly, we build a skin Gaussian model in Cg-Cr color space. Secondly, a calculation of correlation coefficient is performed between the given template and the candidates. Experimental results demonstrate that our system has achieved high detection rates and low false positives over a wide range of facial variations in color, position and varying lighting conditions.

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Mapping Fluvial Landforms with Deep Similarity Learning

10.5194/egusphere-egu21-811 ◽

2021 ◽

Author(s):

Patrice Carbonneau

Keyword(s):

Deep Learning ◽

Image Classification ◽

Color Image ◽

A Priori ◽

Similarity Learning ◽

Dimension Vector ◽

Learning Methods ◽

River Meandering ◽

Sentinel 2 ◽

Training Sets

Semantic image classification as practised in Earth Observation is poorly suited to mapping fluvial landforms which are often composed of multiple landcover types such as water, riparian vegetation and exposed sediment. Deep learning methods developed in the field of computer vision for the purpose of image classification (ie the attribution of a single label to an image such as cat/dog/etc) are in fact more suited to such landform mapping tasks. Notably, Convolutional Neural Networks (CNN) have excelled at the task of labelling images. However, CNN are notorious for requiring very large training sets that are laborious and costly to assemble. Similarity learning is a sub-field of deep learning and is better known for one-shot and few-shot learning methods. These approaches aim to reduce the need for large training sets by using CNN architectures to compare a single, or few, known examples of an instance to a new image and determining if the new image is similar to the provided examples. Similarity learning rests on the concept of image embeddings which are condensed higher-dimension vector representations of an image generated by a CNN. Ideally, and if a CNN is suitably trained, image embeddings will form clusters according to image classes, even if some of these classes were never used in the initial CNN training.&#160;In this paper, we use similarity learning for the purpose of fluvial landform mapping from Sentinel-2 imagery. We use the True Color Image product with a spatial resolution of 10 meters and begin by manually extracting tiles of 128x128 pixels for 4 classes: non-river, meandering reaches, anastomosing reaches and braiding reaches. We use the DenseNet121 CNN topped with a densely connected layer of 8 nodes which will produce embeddings as 8-dimension vectors. We then train this network with only 3 classes (non-river, meandering and anastomosing) using a categorical cross-entropy loss function. Our first result is that when applied to our image tiles, the embeddings produced by the trained CNN deliver 4 clusters. Despite not being used in the network training, the braiding river reach tiles have produced embeddings that form a distinct cluster. We then use this CNN to perform few-shot learning with a Siamese triplet architecture that will classify a new tile based on only 3 examples of each class. Here we find that tiles from the non-river, meandering and anastomising class were classified with F1 scores of 72%, 87% and 84%, respectively. The braiding river tiles were classified to an F1 score of 80%. Whilst these performances are lesser than the 90%+ performances expected from conventional CNN, the prediction of a new class of objects (braiding reaches) with only 3 samples to 80% F1 is unprecedented in river remote sensing. We will conclude the paper by extending the method to mapping fluvial landforms on entire Sentinel-2 tiles and we will show how we can use advanced cluster analyses of image embeddings to identify landform classes in an image without making a priori decisions on the classes that are present in the image.

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An Investigation of Deep Learning Models for EEG-Based Emotion Recognition

Frontiers in Neuroscience ◽

10.3389/fnins.2020.622759 ◽

2020 ◽

Vol 14 ◽

Author(s):

Yaqing Zhang ◽

Jinling Chen ◽

Jen Hong Tan ◽

Yuxuan Chen ◽

Yunyi Chen ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Emotion Recognition ◽

Real Life ◽

Learning Rate ◽

Learning Models ◽

Learning Methods ◽

Time Frequency ◽

Comparison Results

Emotion is the human brain reacting to objective things. In real life, human emotions are complex and changeable, so research into emotion recognition is of great significance in real life applications. Recently, many deep learning and machine learning methods have been widely applied in emotion recognition based on EEG signals. However, the traditional machine learning method has a major disadvantage in that the feature extraction process is usually cumbersome, which relies heavily on human experts. Then, end-to-end deep learning methods emerged as an effective method to address this disadvantage with the help of raw signal features and time-frequency spectrums. Here, we investigated the application of several deep learning models to the research field of EEG-based emotion recognition, including deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and a hybrid model of CNN and LSTM (CNN-LSTM). The experiments were carried on the well-known DEAP dataset. Experimental results show that the CNN and CNN-LSTM models had high classification performance in EEG-based emotion recognition, and their accurate extraction rate of RAW data reached 90.12 and 94.17%, respectively. The performance of the DNN model was not as accurate as other models, but the training speed was fast. The LSTM model was not as stable as the CNN and CNN-LSTM models. Moreover, with the same number of parameters, the training speed of the LSTM was much slower and it was difficult to achieve convergence. Additional parameter comparison experiments with other models, including epoch, learning rate, and dropout probability, were also conducted in the paper. Comparison results prove that the DNN model converged to optimal with fewer epochs and a higher learning rate. In contrast, the CNN model needed more epochs to learn. As for dropout probability, reducing the parameters by ~50% each time was appropriate.

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Vegetation Detection Using Deep Learning and Conventional Methods

Remote Sensing ◽

10.3390/rs12152502 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2502 ◽

Cited By ~ 3

Author(s):

Bulent Ayhan ◽

Chiman Kwan ◽

Bence Budavari ◽

Liyun Kwan ◽

Yan Lu ◽

...

Keyword(s):

Deep Learning ◽

Autonomous Navigation ◽

Vegetation Index ◽

Color Images ◽

Detection Methods ◽

Growth Monitoring ◽

Learning Methods ◽

Object Based ◽

Detection Approach ◽

Conventional Methods

Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer vision, and machine learning (ML) techniques, is also proposed. The vegetation detection methods were applied to high-resolution airborne color images which consist of RGB and near-infrared (NIR) bands. RGB color images alone were also used with the two deep learning methods to examine their detection performances without the NIR band. The detection performances of the deep learning methods with respect to the object-based detection approach are discussed and sample images from the datasets are used for demonstrations.

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