Effective and reliable Hand Detection Using Neural Network with ICA features

독립 성분 특징을 적용한 신경망을 이용한 효율적이고 안정적인 손 검출

In this paper we propose an effective and reliable hand detection method using neural network with ICA(Independent Component Analysis) Features. Many algorithms of hand detection have been proposed yet. Among them, ICA is the one of the interesting topics in image processing. ICA can not only separate mixed signals but also efficiently extract low-dimensional features in signals. ICA features are able to represent the characteristic of the images well. The object of this paper is to use effectively ICA that has above advantage. That is, by the proper number of Independent component the arithmetic speed is faster and by normalization of ICA feature the performance of detection is more reliable. For this, we adopt the algorithm, the Proportion of variance, which select the ICA feature by comparing the ratio of variance of ICA feature. By this method, we can extract the feature that is good at classifying hand and non-hand. Our experimental results show that by using ICA features, we obtained a better performance in hand detection than by only training NN on the image. And we can use hand detection system effectively and reliably by our proposal.