Kamalesh V N

Work place: Department of CS&E, TJIT, Bangalore, Karnataka, India

E-mail: kamalesh.v.n@gmail.com


Research Interests: Image Processing, Image Manipulation, Image Compression, Pattern Recognition


Kamalesh V N is Professor and Dean in the Department of Computer Science and Engineering, T John Institute of Technology, Bangalore, Karnataka, India. Previously, he worked as Special Officer for e-Learning Department, Visvesvaraya Technological University, Mysore Region. He obtained Ph.d in Computer Science and Engineering from Satyabhama University. He is having 23 years of experience in academics, research and administration. He has published several papers in the international and national journals and in the conference proceedings. He received national and international awards for academics and research. His area of research is algorithms, biometrics, graph algorithms, image Processing, pattern recognition and technology for education.

Author Articles
An Efficient Gait Recognition Approach for Human Identification Using Energy Blocks

By Manjunatha Guru V G Kamalesh V N Dinesh R

DOI: https://doi.org/10.5815/ijigsp.2017.07.05, Pub. Date: 8 Jul. 2017

Human gait recognition is an emerging research topic in the biometrics research field. It has recently gained a wider interest from machine vision research community because of its rich amount of merits. In this paper, a robust energy blocks based approach is proposed. For each silhouette sequence, gait energy image (GEI) is generated. Then it is split into three blocks, namely lower legs, upper-half and head. Further, Radon transform is applied to three energy blocks separately. Then, standard deviation is used to capture the variation in radial axis angle. Finally, support vector machine classifier (SVM) is effectively used for the classification procedures. The more prominent gait covariates such as multi views, backpack, carrying, least number of frames, clothing and different walking speed conditions are effectively addressed in this work by choosing sequential, even, odd and multiple’s of three numbering frames for each sequence. Extensive experiments are conducted on four considerably large, publicly available standard datasets and the promising results are obtained.

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