Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/206751
Title: Development of Palm Vein Recognition Technique
Researcher: Raut Shivram Dagadu
Guide(s): Humbe V. T.
Keywords: Palm vain recognition
University: Swami Ramanand Teerth Marathwada University
Completed Date: 05/05/2017
Abstract: The biometric is an automated way to recognize a person based on physiological or behavioral characteristics. The human blood vascular structure is a basis of the biometric recognition system. The palm vein trait is a part of physiological biometric characteristics and it is a network of blood vascular structure lies under the skin at palm region of the hand. A vein being blood vessel carries deoxygenated blood from body part to heart. This deoxygenated blood contains hemoglobin that absorbs near infrared illumination and reflects lighter shade to visualize a vein structure. The observations were made to consider palm vein as superior trait compared to traditional biometric characteristics, because as it lies under the skin and so it s hard to duplicate or counterfeit. It is unique and distinct in its vascular structure that even identical twin does have a different form of structure. The blood vascular structure does not get change throughout a lifetime of an individual. So, all these characteristics make palm vein biometric a superior trait, compared to traditional biometric characteristics. The image database enrolled with 250 different subjects having 3,000 images of each right and left palm. The access to this image database developed by Hong Kong Polytechnic University (PolyU) was obtained to test the hypothesis. The objective of research work was focused on analyzing blood vascular structure at palm region of hand, extraction and detection of distinct features and finally the development of recognition technique based on unique feature characteristic. newlineThe proposed research work is planned using schemes of pattern recognition design cycle that mainly includes steps such as data preprocessing, feature extraction and detection, followed by feature matching. The image data preprocessing was performed using enhancement operations, various filtering algorithms and morphological operations. The step of feature extraction is worked out using efficient implementation of Gabor filter and canny edge detector. The res
Pagination: 99p
URI: http://hdl.handle.net/10603/206751
Appears in Departments:School of Computational Sciences

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01_title.pdfAttached File14.6 kBAdobe PDFView/Open
02_certificate.pdf119.69 kBAdobe PDFView/Open
03_abstract.pdf136.37 kBAdobe PDFView/Open
04_declaration.pdf89.05 kBAdobe PDFView/Open
05_acknowledgement.pdf9.93 kBAdobe PDFView/Open
06_contents.pdf94.87 kBAdobe PDFView/Open
07_list_of_tables.pdf82.31 kBAdobe PDFView/Open
08_list_of_figures.pdf132.39 kBAdobe PDFView/Open
09_abbreviations.pdf92.46 kBAdobe PDFView/Open
10_chapter 1.pdf451.49 kBAdobe PDFView/Open
11_chapter 2.pdf344.6 kBAdobe PDFView/Open
12_chapter 3.pdf556.12 kBAdobe PDFView/Open
13_chapter 4.pdf1.37 MBAdobe PDFView/Open
14_conclusions.pdf88.68 kBAdobe PDFView/Open
15_summary.pdf139.59 kBAdobe PDFView/Open
16_bibliography.pdf255.11 kBAdobe PDFView/Open


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