V. T. Humbe

Work place: Swami Ramanand Teerth Marathwada University, Sub-Centre, Ausa Road, Peth, Latur (MS) 413531, India

E-mail: vikashumbe@gmail.com

Website: https://scholar.google.co.in/citations?user=HAKtroEAAAAJ&hl=en

Research Interests: Image Processing, Computer Vision, Biometrics

Biography

Dr. Vikas T. Humbe has completed his Ph.D. degree from University Department of Computer Science and Information Technology, Dr.B.A.M.U. Aurangabad and working as the Assistant Professor at Department of Computer Science, School of Technology, S.R.T.M. University, Nanded, Sub-campus, Latur. In teaching, he has been focusing on Digital Image and video processing concepts and Problem Based Learning approaches in Computer Science Education. In research, his current interests include Pattern Recognition, Image and video based processing, Data ware housing and web mining etc. He has published 42 research articles at National/International conferences and journals also he is the author of the books. He is reviewer for various International and National Journals and Conferences like Elsevier‘s Pattern Recognition Letters, Journal on Machine Vision and Applications, Academic Journals, IEEE IJCNN 07 and 09, ACVIT-09 etc. He is Member of IACSIT Singapore, Member of IAEng, Hong Kong, CSTA, USA and Graduate Member of IEEE USA. His area of research interest is Biometrics, Image Processing, Computer Vision and Video Processing. He is the IEEE Graduate Member and has immense research recognition worldwide.

Author Articles
An Approach to Boundary Extraction of Palm Lines and Vein Pattern

By Shriram D. Raut V. T. Humbe

DOI: https://doi.org/10.5815/ijigsp.2014.12.07, Pub. Date: 8 Nov. 2014

The palm vein biometrics is automated tool to recognize a person based on human vein pattern. The vein pattern is intrinsic and subcutaneous so that is very difficult to forge or fake. This paper discusses about the feature extraction of the hand based recognition system that involves features like vein pattern, principal lines and secondary lines. The morphological operations such as opening, closing and edge detection technique like canny algorithm are used to extract the feature set. The result shows the prominent feature extraction using image processing techniques.

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