A Real-time Hand Gesture Recognition Technique and Its Application to Music Display System

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 threedimensional 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 Ho-Joon Kim received the B.S. degree in Computer Engineering from

Human-like sign-language learning method using deep learning

“…Sign language involves the use of gestures. Thus, this section introduces gesture-recognition methods, which can be divided into two categories, namely hand-gesture recognition [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]21] and non-hand-gesture recognition [22,23]. Because the proposed method is a hand-gesture recognition method, this section introduces several competitive approaches from among the developed hand-gesture recognition methods.…”