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.

Gait Recognition System Using Gabor Wavelet and Active Gait Differential Image

“…The result in terms of accuracy of the developed AGDI feature extraction with Gabor filter method was compared with earlier methods as shown in Table 2. Gait flow image [17], Enhanced Gait Energy Image [18], Frame Differential Energy Image (FDEI) [2], Structural Gait Differential Image (SGDI) [19], Gait Energy Image (GEI) [19], Gait Entropy Image (GEnI) [20] gave 98%, 75%, 95%, 89.29%, 60.37%, 80.1% respectively as against 99.19% by the developed AGDI with Gabor filter method. Table 3.…”

Recognition of a Person Wearing Sport Shoes or High Heels through Gait Using Two Types of Sensors

“…When it comes to classifiers hidden Markov models (HMM) [ 27 ], support vectors machine (SVM) [ 28 ], k-nearest neighbors [ 29 , 30 ], neural networks [ 31 ] or deep learning [ 32 ] are often utilized. Additionally, to improve the quality of obtained results, ensemble classifiers are being used more and more often [ 33 , 34 ].…”