Gait feature extraction and recognition in biometric system

Abstract

This research focus on the development an automatic human identification system using gait sequence images. Human identification is widely used in computer vision applications such as surveillance system, criminal investigations and human-computer interaction. Gait sequence image is a nonstationary data and can be modelled using a statistical learning technique. The propose technique consists of three different stages. The pre-processing stage computes the average silhouette images to capture the important information and get a better representation for gait silhouette data. Then a principle component analysis (PCA) technique is applied on the average silhouette to extract the important gait features and reduce a dimension of gait data. A linear projection method used in this stage is able to reduce redundant features and remove noise data from the gait image. Furthermore, this approach will increase a discrimination power in the feature space when dealing with low frequency information. Low dimensional feature distribution in the feature space is assumed Gaussian, thus the Euclidean distance classifier can be used in the classification stage. The propose algorithm is a model-free based which uses gait silhouette features for the compact gait image representation and a linear feature reduction technique to remove redundant and noise information. The proposed algorithm has been tested using a benchmark CASIA datasets. The experimental results show that the best recognition rate is 90%