| Type of Material: | Thesis |
| Title: | Iris Feature Extraction for IRIS Recognition At A Distance |
| Researcher: | SWATI DATTATRAYA SHIRKE |
| Guide: | RAJABHUSHNAM, C |
| Department: | Department of Engineering and Technology(Computer Science and Engineering) |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2020 |
| Language: | English |
| Subject: | Computer Science | Computer Science Information Systems | Engineering and Technology | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai; 2020; D15CS503 |
| Fulltext: | Shodhganga |
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| 001 | 454684 | |
| 003 | IN-AhILN | |
| 005 | 2024-09-18 12:28:49 | |
| 008 | __ | 240918t2020||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_454684 |
| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aSWATI DATTATRAYA SHIRKE|eResearcher |
| 110 | __ | |aDepartment of Engineering and Technology(Computer Science and Engineering)|bBharath University, Chennai|dChennai|ein |
| 245 | __ | |aIris Feature Extraction for IRIS Recognition At A Distance |
| 260 | __ | |aChennai|bBharath University, Chennai|c2020 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cDepartment of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai|d2020|oD15CS503 |
| 520 | __ | |aThe primary intention of this research is to design and develop a technique for Iris Recognition at-a Distance (IAAD) by proposing optimized machine learning algorithm. The overall procedure of the proposed technique is as follows: Initially, the input iris image will be subjected to pre-processing and then, the iris region will be extracted from the pre-processed image using Hough transform. Once the iris region is extracted, iris segmentation and normalization will be done using Daugman s rubber sheet model. Then, the feature extraction will be carried out by developing a model, named ScatTLOOP, that extracts the features using Local Optimal Oriented Pattern (LOOP) descriptor, scattering transform, and tartlet transform. Based on the features extracted, the Neural Network (NN) will be trained using the proposed Chronological Monarch Butterfly Optimization (Chronological-MBO) algorithm, which will be developed by modifying the MBO using chronological concept. Thus, the proposed ChronologicalMBO based NN wi |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Information Systems |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aRAJABHUSHNAM, C|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/310476|yShodhganga |
| 905 | __ | |afromsg |
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