On combining gait features

This paper describes a method of gait recognition using multiple gait features in conjunction with score-level fusion techniques. More specifically, we focus on the state-of-the-art period-based gait features such as a gait energy image, a frequency-domain feature, a gait entropy image, a chrono-gait image, and a gait flow image. In addition, we employ various types of the score-level fusion approaches including not only conventional transformation-based approaches (e.g., sum-rule and min-rule) but also classification-based approaches (e.g., support vector machine) and density-based approaches (e.g., Gaussian mixture model, kernel density estimation, linear logistic regression). In experiments, the large-population gait database with 3,249 subjects was used to measure the performance improvement in a statistically reliable way. The experimental results show 7% relative improvement on average with regard to equal error rate of the false acceptance rate and false rejection rate in verification scenarios, and also show 20% reduction of the number of candidates to be checked under 1% misdetection rate on average in screening tasks.