Features Mapping Based Human Gait Recognition

Abstract

Gait recognition is the term used for detection of Human based on the features. The Feature extraction and Feature Mapping is the main aspect to recognize the Gestures from the Database of features. Recognition of any individual is a task to identify people. Human recognition methods such as face, fingerprints, and iris generally require a cooperative subject, physical contact or close proximity. These methods are not able to recognize an individual at a distance therefore recognition using gait is relatively new biometric technique without these disadvantages. Human identification using Gait is method to identify an individual by the way he walk or manner of moving on foot. Gait recognition is a type of biometric recognition and related to the behavioral characteristics of biometric recognition. Gait offers ability of distance recognition or at low resolution. This project aims to recognize an individual using his gait features. However the majority of current approaches are model free which is simple and fast but we will use model based approach for feature extraction and for matching of parameters with database sequences. After matching of Features, the Images have been identified and show the dataset from it matched. The Results are accurate and shows efficiency. In this firstly binary silhouette of a walking person is detected from each frame of an image. Then secondly, the features from each frame are extracted using the image processing operation. In the end SVM, K-MEANS and LDA are used for training and testing purpose. Every experiment and test is done on CASIA database. The results in this paper are better and improved from previous results by using SVM , K MEANS.
DOI: 10.17762/ijritcc2321-8169.150672