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Performance Improvement Using an Automation System for Segmentation of Multiple Parametric Features Based on Human Footprint
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 Title & Authors
Performance Improvement Using an Automation System for Segmentation of Multiple Parametric Features Based on Human Footprint
Kumar, V.D. Ambeth; Malathi, S.; Kumar, V.D. Ashok; Kannan, P.;
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Rapid increase in population growth has made the mankind to delve in appropriate identification of individuals through biometrics. Foot Print Recognition System is a new challenging area involved in the Personal recognition that is easy to capture and distinctive. Foot Print has its own dimensions, different in many ways and can be distinguished from one another. The main objective is to provide a novel efficient automated system Segmentation using Foot Print based on structural relations among the features in order to overcome the existing manual method. This system comprises of various statistical computations of various foot print parameters for identifying the factors like Instep-Foot Index, Ball-Foot Index, Heel- Index, Toe- Index etc. The input is naked footprint and the output result to an efficient segmentation system thereby leading to time complexity.
Human footprint;Multiple features;Preprocessing;Segmentation;Gaussian filter;Cropping;Region growing;
 Cited by
R. Sukthankar and R. Stockton, “Argus: the digital doorman”, IEEE Intelligent Systems Vol. 16, Issue 2, 2001.

S. Liu and M. Silverman, “A practical guide to biometric security Technology”, IEEE IT Pro, pp. 27-32, Jan-Feb., 2001.

Weidong Wang, Xijian Ping and Yihong Ding (2004), “Footprint Heavy Pressure Surface Pick-up and description”, Proceedings of the Third international Conference on image and Graphics, IEEE First Symposium on Multi Agent Security and Survivability, pp. 278-281.

Sean W., Yip B.S. and Thomas E. Prieto (1996), “A System for Force Distribution Measurement beneath the feet”, IEEE Int. Conference on Biomedical Engineering, pp. 32-34.

Liu Guozhong, Wang Boxiong, Shi Hui, Luo Xiuzhi and Wang Rui (2006), “Measurement system for 3-D shapes under Different Loads”, International Technology and Innovation Conference, pp. 246-250

Rong Wang, Fanliang Bu, Hua Jin and Lihua Li (2007), “Toe shaped recognition algorithm based on fuzzy neural networks”, IEEE Third International Conference on Natural Computation (ICNC 2007), Vol. 2, pp.734-741.

Jaeseok Yun, Gregory Abowd, Woontack Woo and Jeha Ryu (2007), “Biometric user identification with-dynamic footprint”, IEEE Int. Conference on Bio Inspired Computing: Theories and Applications, pp. 225-230.

Wei Jia, Hai-Yang Cai, Jie Gui, Rong-Xiang Hu, Ying-Ke Lei, Xiao-Feng Wang (2011), “Newborn footprint recognition using orientation feature”, Neural Computer and Applications, Springer.

V.D. Ambeth Kumar and M.Ramakrishnan, “A comparative study of fuzzy evolutionary techniques for footprint recognition and performance improvement using wavelet-based fuzzy neural network”, Int. J. of Computer Applications in Technology, 2013 Vol. 48, No. 2, pp. 95-105. crossref(new window)

Suneel R. Qamra, “Naked foot marks–A preliminary study of identification factors”, Forensic Science International, Volume 16, Issue 2, September-October 1980, Pages 145-152. crossref(new window)