Continuous Gesture Recognition using HLAC and Low-Dimensional Space

Abstract

This study proposes a continuous gesture recognition using information about features of motions obtained from continuous gesture images through higher order correlation feature coefficient and principal component analysis (PCA). The proposed method, first, separates two-dimensional silhouette gesture region from a continuous input image that includes a human body image. Information about 35 features are extracted using the higher order local auto correlation coefficient in the divided image and a low-dimensional gesture space is composed using PCA. The model feature value reflected in the gesture space is composed of symbols of certain conditions through clustering algorithm so as to be used as the input symbol of a hidden marker model, and a random input motion is recognized as the relevant gesture model with the highest probability value. The proposed method has less computation workload than the previous geometric feature-based method or appearance-based method and shows a high recognition rate with using the minimum information, so it is very suitable for the construction of a real-time system. In addition, the recognized gesture can be used input data of various applications like dynamic motion type game by mapping to an operating system. And the human motions can be used the objects of observation, such as smart home and behavior analysis in special spaces.