Personal identification based on feature extraction using motions of a reduced set of joints

Abstract

It is required to construct a practical scheme that can obtain personal properties accurately based on gait analysis because it can work under severe conditions. The authors tackle this task and have proposed a feature extraction scheme based on joint motions. However, it is not clear that our scheme is feasible because the scheme uses 25 joints all of that cannot be estimated accurately in severe conditions based on only visible images for feature extraction. To show that our scheme has a potential for practical applications, the classification accuracy of personal identification is evaluated under practical conditions where the number of joints available for feature extraction is small. Experimental results show that the classification accuracy is 81.58% if the number of joints is 10 and the accuracy keeps 79.82% even if the number of joints is reduced to 6.