Gait Recognition Based on Silhouettes Sequences and Neural Networks for Human Identification

Abstract

Human gait recognition is moving ahead by the need for automated person identification and verification at a distance in many applications. In this paper our system presented silhouette videos and neural networks based system for human identification. For each sequence of silhouette images, an automated Region of Interest (ROI) algorithm applied to reduce dimensionality, extract gait features has been attempted with 3level 2Dimension Discrete Wavelet Transform (3L-2D-DWT), edges detection and gait cycle were used to extract relevant feature. Back propagations neural networks used as pattern recognition. The M ATLAB software graphics user interface is designed to display result and simplify the use of the system. The developed system has been evaluated using (TUM-IITKGP) which contains three different type of walking categories and the results demonstrate that the proposed system achieves 98.8%, 95.87% and 88% for normal walk, walking with backpack and walking with hand in pocket category respectively of correct recognition. We concluded that the different side of view movement increase reliability of the key extracted feature and improve the neural network performance which opens a scope for further development.

Human Gait Recognition using Neural Network Multi-Layer Perceptron

“…building an appropriate authenticated continuously. MV can implemented in two ways MV based free model (Mohualdeen & Baker, 2018) .…”