Abstract
Physical traits such as the shape of the hand and face can be used for human recognition and identification in video surveillance systems and in biometric authentication smart card systems, as well as in personal health care. However, the accuracy of such systems suffers from illumination changes, unpredictability, and variability in appearance (e.g. occluded faces or hands, cluttered backgrounds, etc.). This work evaluates different statistical and chrominance models in different environments with increasingly cluttered backgrounds where changes in lighting are common and with no occlusions applied, in order to get a reliable neural network reconstruction of faces and hands, without taking into account the structural and temporal kinematics of the hands. First a statistical model is used for skin colour segmentation to roughly locate hands and faces. Then a neural network is used to reconstruct in 3D the hands and faces. For the filtering and the reconstruction we have used the growing neural gas algorithm which can preserve the topology of an object without restarting the learning process. Experiments conducted on our own database but also on four benchmark databases (Stirling’s, Alicante, Essex, and Stegmann’s) and on deaf individuals from normal 2D videos are freely available on the BSL signbank dataset. Results demonstrate the validity of our system to solve problems of face and hand segmentation and reconstruction under different environmental conditions.
Highlights
Over the last decades, there has been an increasing interest in using neural networks and computer vision techniques to allow users to directly explore and manipulate objects in a natural and intuitive environment without the use of electromagnetic tracking systems
We have compared the performance of different probabilistic colour models and colour spaces for skin segmentation as an initialisation stage for the growing neural gas (GNG) algorithm
Based on the capabilities of GNG to readjust to new input patterns without restarting the learning process, we are interested in reducing meaningless image data by taking into consideration that human skin has a relatively unique colour and applying appropriate parametric skin distribution modelling
Summary
There has been an increasing interest in using neural networks and computer vision techniques to allow users to directly explore and manipulate objects in a natural and intuitive environment without the use of electromagnetic tracking systems. Since this method is too complicated to implement, the most widespread alternative is the feature-based method [26] where features such as the geometric properties of the hand or face are analysed using either neural networks (NNs) [47, 52] or stochastic models such as hidden Markov models (HMMs) [11, 49] This is feasible because of the emergence of cheap 3D sensors capable of providing a real-time data stream and enabling feature-based computation of three-dimensional environment properties like curvature, an approach closer to human learning procedures. Another approach for both faces and hands is to use a skin colour classifier [27].
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