Abstract—In the paper, we introduce a real-time hand gesture recognition method using a neural network. The underlying system is an automatic music display system which consists of three modules; feature extraction module, pattern classification module, and display control module. To reduce the computation time of the feature extraction process and the pattern classification process, a three-dimensional data representation called motion history volume has been adopted. In addition, we propose a feature selection technique based on a modified fuzzy min-max neural network. We have defined a relevance factor which can measure the relevance of a feature to classify the specific pattern classes. The feature selection method can remove ineffective features and erroneous features in the learning data set by using the relevance factor data.

Index Terms—hand gesture recognition, motion history volume, feature selection

Cite: Jun-Yong Lee, Joong-Eun Jung, and Ho-Joon Kim, "A Real-time Hand Gesture Recognition Technique and Its Application to Music Display System," Jounal of Automation and Control Engineering, Vol. 4, No. 2, pp. 177-180, April, 2016. doi: 10.12720/joace.4.2.177-180