HUMAN IDENTIFICATION USING TEMPORAL

NANO SCIENTIFIC RESEARCH CENTRE PVT.LTD.,  AMEERPET, HYD

WWW.NSRCNANO.COM, 09640648777, 09652926926

JAVA PROJECTS LIST--2013

JAVA 2013 IEEE PAPERS

HUMAN IDENTIFICATION USING TEMPORAL

Abstract

            Gait Energy Image (GEI) is an efficient template for human identification by gait. However, such a template loses temporal information in a gait sequence, which is critical to the performance of gait recognition. To address this issue, we develop a novel temporal template, named Chrono-Gait Image (CGI), in this paper. The proposed CGI template first extracts the contour in each gait frame, followed by encoding each of the gait contour images in the same gait sequence with a multichannel mapping function and compositing them to a single CGI. To make the templates robust to a complex surrounding environment, we also propose CGI-based real and synthetic temporal information preserving templates by using different gait periods and contour distortion techniques. Extensive experiments on three benchmark gait databases indicate that, compared with the recently published gait recognition approaches, our CGI-based temporal information preserving approach achieves competitive performance in gait recognition with robustness and efficiency.

Existing System:

            Unlike other biometric features such as iris, faces, palm, and fingerprint, the advantages of gait include:

1) Gait can be collected in a non-contactable, noninvasive, and hidden manner;

2) Gait is the only perceptible biometric at a distance. However, the performance of gait recognition suffers from some exterior factors such as clothing, shoes, briefcases, and environmental context. Furthermore, whether or not the spatiotemporal relationship between gait frames in a gait sequence is effectively represented also influences the performance of gait recognition systems. Although it is a challenging task, the nature of gait indicates that it is an irreplaceable biometric and can benefit the remote biometric authentication.

Proposed System:

            To build a successful gait recognition system, feature extraction plays a crucial role. Currently, gait feature extraction methods can be roughly divided into two major categories: model-based and model-free approaches. Modelbased approaches assume that the gait can be modeled with a structure/motion model. However, it is not easy to extract the underlying model from gait sequences. Modelfree approaches either keep temporal information in the recognition (and training) stage, or convert a sequence of images into a single template. Although some model-free approaches such as Gait Energy Image (GEI) have attractively low computational cost, such a conversion may lose the temporal information of gait sequences.

Software Requirement Specification

Software Specification

Operating System       :           Windows XP

Technology                 :           JAVA 1.6, JMF

Hardware Specification

Processor                     :           Pentium IV

RAM                           :           512 MB

Hard Disk                   :           80GB