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Enhanced Human Identity and Gender Recognition from Gait Sequences using SVM and MDA

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International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 119 - Number 2
Year of Publication: 2015
Authors:
Alka Saini
Harpreet Singh
10.5120/21037-3358

Alka Saini and Harpreet Singh. Article: Enhanced Human Identity and Gender Recognition from Gait Sequences using SVM and MDA. International Journal of Computer Applications 119(2):6-9, June 2015. Full text available. BibTeX

@article{key:article,
	author = {Alka Saini and Harpreet Singh},
	title = {Article: Enhanced Human Identity and Gender Recognition from Gait Sequences using SVM and MDA},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {119},
	number = {2},
	pages = {6-9},
	month = {June},
	note = {Full text available}
}

Abstract

The identification through biometric is a better way because it associate with individual not with information passing from one place to another. There are numerous biometric measures which can be used to help derive an individual identity. It is the biometric process and has many advantages over other biometric traits such as face, iris, fingerprint, palm print, etc. Most current approaches make the unrealistic assumption that persons walk along a fixed direction or a pre-defined path. Gait is the manner or style of moving on foot. Human Gait recognition identifies the individuals by the way in which they walk. Recognition of an individual is an important task to identify people. A gait sequence is collected from arbitrary walking directions. In this paper we present the approach of human identity and gender recognition using Model based features extraction and SURF for matching along with SVM and MDA algorithm.

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