Automatic Gait Gender Classification Using Convolutional Neural Networks

Abstract

In this study, automatic gait gender classification using convolutional neural networks includes three phases: i) human gait signature generation, ii) which convolves the gait energy images with filters for feature extraction and iii) classified using feed-forward convolutional neural networks. Analysed performance of Gabor and Log Gabor features using classification accuracy. The Log Gabor filter's accuracy was 92.11% for the Normal vs Normal dataset, 74.14% for the Normal vs Bag dataset, 46.55% for the Normal vs Coat dataset, 72.41% for the Normal vs Case dataset and whiles Gabor filter's accuracy was 75% for the Normal vs Normal dataset, 60.34% for the Normal vs Bag dataset 65.52% for the Normal vs Coat dataset and 55.17% for the Normal vs Case dataset.