Analysis of Gait Flow Image and Gait Gaussian Image Using Extension Neural Network for Gait Recognition

Abstract

This paper proposes a new technique to recognize human gait by combining model free feature extraction approaches and a classifier. Gait flow image (GFI) and gait Gaussian image (GGI) are the two feature extraction techniques used in combination with ENN. GFI is a gait period based technique, uses optical flow features. So it directly focuses on dynamic part of human gait. GGI is another gait period based technique, computed by applying Gaussian membership function on human silhouettes. Next, ENN has been used as a classifier which combines the extension theory and neural networks. All the study has been done on CASIA-A and OU-ISIR treadmill B databases. The results derived using ENN are compared with SVM (support vector machines) and NN (Nearest neighbor) classifiers. ENN proved to give good accuracy and less iteration as compared to other traditional methods.

Utilizing Spatio Temporal Gait Pattern and Quadratic SVM for Gait Recognition

“…The optical flow-based gait feature extraction is computed on pixel-wise apparent motion estimation. The optical flow-based research work includes [84][85][86][87]. The gait optical flow image is further processed with LDA (Linear Discriminant Analysis) and PCA (Principal Component Analysis) for gait recognition robust to appearance and speed of walk [88].…”

“…The optical flow computed between two gait silhouette images reflects the relative motion of the human body. The GFI-based gait recognition includes studies [86,89,90].…”