A reliable human skin detection method that is adaptable to different human skin colors and illumination conditions is essential for better human skin segmentation. Even though different human skin-color detection solutions have been successfully applied, they are prone to false skin detection and are not able to cope with the variety of human skin colors across different ethnic. Moreover, existing methods require high computational cost. In this paper, we propose a novel human skin detection approach that combines a smoothed 2-D histogram and Gaussian model, for automatic human skin detection in color image(s). In our approach, an eye detector is used to refine the skin model for a specific person. The proposed approach reduces computational costs as no training is required, and it improves the accuracy of skin detection despite wide variation in ethnicity and illumination. To the best of our knowledge, this is the first method to employ fusion strategy for this purpose. Qualitative and quantitative results on three standard public datasets and a comparison with state-of-the-art methods have shown the effectiveness and robustness of the proposed approach.
Other approaches are multilayer perceptron @cite_17 @cite_8 @cite_18 , Bayesian classifiers @cite_0 @cite_20 @cite_9 and random forest @cite_26 . In multilayer perceptron based skin classification, a neural network is trained to learn the complex class conditional distributions of the skin and non-skin pixels. @cite_17 proposed a Kohonen network-based skin detector where two Kohonen networks; skin only and skin plus non-skin detectors were trained from a set of about 500 manually labelled images to obtain an optimal result. @cite_0 used a Bayesian network with training data of 60,000 samples for skin modelling and classification. @cite_20 proposed the use of tree-augmented Naive Bayes classifiers for skin detection. The Bayesian decision rule to minimum cost is a well-established technique in statistical pattern classification. Jones and Rehg @cite_9 used the Bayesian decision rule with a 3D @math histogram model built from 2 billion pixels collected from 18,696 web images to perform skin detection. Readers are encourage to read @cite_12 @cite_14 for a detailed state of the art review. Although these solutions had been very successful, they suffer from a tradeoff between precision and computational complexity.
@cite_18 @cite_26 @cite_14 @cite_8 @cite_9 @cite_0 @cite_12 @cite_20 @cite_17
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We propose a novel method called deep convolutional decision jungle (CDJ) and its learning algorithm for image classification. The CDJ maintains the structure of standard convolutional neural networks (CNNs), i.e. multiple layers of multiple response maps fully connected. Each response map-or node-in both the convolutional and fully-connected layers selectively respond to class labels s.t. each data sample travels via a specific soft route of those activated nodes. The proposed method CDJ automatically learns features, whereas decision forests and jungles require pre-defined feature sets. Compared to CNNs, the method embeds the benefits of using data-dependent discriminative functions, which better handles multi-modal heterogeneous data; further,the method offers more diverse sparse network responses, which in turn can be used for cost-effective learning classification. The network is learnt by combining conventional softmax and proposed entropy losses in each layer. The entropy loss,as used in decision tree growing, measures the purity of data activation according to the class label distribution. The back-propagation rule for the proposed loss function is derived from stochastic gradient descent (SGD) optimization of CNNs. We show that our proposed method outperforms state-of-the-art methods on three public image classification benchmarks and one face verification dataset. We also demonstrate the use of auxiliary data labels, when available, which helps our method to learn more discriminative routing and representations and leads to improved classification. Problem statement: Skin color detection is used as a preliminary step in numerous computer vision applications like face detection, nudity recognition, hand gesture detection and person identification. In this study we present a pixel based skin color classification approach, for detecting skin pixels and non skin pixels in color images, using a novel neural network symmetric classifier. The neural classifiers used in the literature either uses a symmetric model with single neuron in the output layer or uses two separate neural networks (asymmetric model) for each of the skin and non-skin classes. The novelty of our approach is that it has two output layer neurons; one each for skin and non-skin class, instead of using two separate classifiers. Thus by using a single neural network classifier we have improved the separability between these two classes, eliminating additional time complexity that is needed in asymmetric classifier. Approach: Skin samples from web images of people from different ethnic groups were collected and used for training. Ground truth skin segmented images were obtained by using semiautomatic skin segmentation tool developed by the authors. The ground truth database of skin segmented images, thus obtained was used to evaluate the performance of our NN based classifier. Results: With proper selection of optimum classification threshold that varies from image to image the classifier gave the detection rate of more than 90 with 7 false positives on an average, Conclusion Recommendations: It is observed that the neural network is capable of detecting skin in complex lighting and background environments. The classifier has the ability to classify the skin pixels belonging to people from different ethnic groups even when they are present simultaneously in an image. The proper choice of optimum classification threshold that varies from image to image is an issue here. Automatic computation of this optimum threshold for each image is desired in practical skin detection applications. This issue can be taken up as a future study, which will enable us to perform fully automatic skin segmentation with reduced false positives. Deep neural networks have been exhibiting splendid accuracies in many of visual pattern classification problems. Many of the state-of-the-art methods employ a technique known as data augmentation at the training stage. This paper addresses an issue of decision rule for classifiers trained with augmented data. Our method is named as APAC: the Augmented PAttern Classification, which is a way of classification using the optimal decision rule for augmented data learning. Discussion of methods of data augmentation is not our primary focus. We show clear evidences that APAC gives far better generalization performance than the traditional way of class prediction in several experiments. Our convolutional neural network model with APAC achieved a state-of-the-art accuracy on the MNIST dataset among non-ensemble classifiers. Even our multilayer perceptron model beats some of the convolutional models with recently invented stochastic regularization techniques on the CIFAR-10 dataset. The automated detection and tracking of humans in computer vision necessitates improved modeling of the human skin appearance. We propose a Bayesian network approach for skin detection. We test several classifiers and propose a methodology for incorporating unlabeled data. We apply the semi-supervised approach to skin detection and we show that learning the structure of Bayesian network classifiers enables learning good classifiers with a small labeled set and a large unlabeled set.