Combining Convolutional and Recurrent Neural Networks for Human Skin Detection

Abstract

Skin detection from images, typically used as a preprocessing step, has a wide range of applications such as dermatology diagnostics, human computer interaction designs and so on. It is a challenging problem due to many factors such as variation in pigment melanin, uneven illumination and differences in ethnicity geographics. Besides, age and gender introduce additional difficulties to the detection process. It is hard to determine whether a single pixel is skin or non-skin without considering the context. An efficient traditional hand-engineered skin color detection algorithm requires extensive work by domain experts. Recently, deep learning algorithms, especially convolutional neural networks (CNNs), have achieved great success in pixel-wise labelling tasks. However, CNN-based architectures are not sufficient for modeling the relationship between pixels and their neighbors. In this letter, we integrate recurrent neural networks (RNNs) layers into the fully convolutional neural networks (FCNs), and develop an end-to-end network for human skin detection. In particular, FCN layers capture generic local features while RNN layers model the semantic contextual dependencies in images.

Existing System

Bayesian Classifier, Gaussian Mixture Model, Support Vector Machine, Neural Network, Random Forest.

Proposed System

Skin detection is the process of discriminating skin and non-skin pixels in a digital image, which plays an important role in multifarious applications ranging from face detection, gesture recognition, dermatology diagnostics, driver fatigue detection, human computer interaction to a variety of computational health informatics. An end-to-end network for human skin detection by integrating recurrent neural layers into fully convolutional neural networks is proposed. RNN layers are employed to model the semantic spatial dependencies between image pixels. Specifically, we use FCN layers to capture generic local features and RNN layers to model the semantic contextual dependencies in images to detect the human skin.

Architecture

BLOCK DIAGRAM