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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 |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 14.6 kB | Adobe PDF | View/Open |
02_certificate.pdf | 119.69 kB | Adobe PDF | View/Open | |
03_abstract.pdf | 136.37 kB | Adobe PDF | View/Open | |
04_declaration.pdf | 89.05 kB | Adobe PDF | View/Open | |
05_acknowledgement.pdf | 9.93 kB | Adobe PDF | View/Open | |
06_contents.pdf | 94.87 kB | Adobe PDF | View/Open | |
07_list_of_tables.pdf | 82.31 kB | Adobe PDF | View/Open | |
08_list_of_figures.pdf | 132.39 kB | Adobe PDF | View/Open | |
09_abbreviations.pdf | 92.46 kB | Adobe PDF | View/Open | |
10_chapter 1.pdf | 451.49 kB | Adobe PDF | View/Open | |
11_chapter 2.pdf | 344.6 kB | Adobe PDF | View/Open | |
12_chapter 3.pdf | 556.12 kB | Adobe PDF | View/Open | |
13_chapter 4.pdf | 1.37 MB | Adobe PDF | View/Open | |
14_conclusions.pdf | 88.68 kB | Adobe PDF | View/Open | |
15_summary.pdf | 139.59 kB | Adobe PDF | View/Open | |
16_bibliography.pdf | 255.11 kB | Adobe PDF | View/Open |
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