Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/342652
Title: | Efficient multimodal biometric system for human authentication using deep belief network and random forest network |
Researcher: | S ARUNARANI |
Guide(s): | R GOBINATH |
Keywords: | Computer Science Computer Science Artificial Intelligence Engineering and Technology |
University: | Vels University |
Completed Date: | 2020 |
Abstract: | In the modernized world, the basic prerequisite of any individual is personal authentication for accessing the information. A control device which is proficient, high- tech, intelligent, efficient, very authentic and work in real time, to process all the applications including personal security to financial security is needed at this hour for all the human . Traditional method of authentication is based on password which consists of symbols or/and text values. Traditional methods of authentication process have many threats of attacks in breaking the password. The drawbacks in traditional methods can be overcome with the usage of individual characteristics usage in authentication process. This method is known as biometric authentication systems. Biometric system use human anatomy or their personality for authentication process. For eg., human face, ear, or voice, keystroke etc., The attributes used in biometric systems cannot be terminated or forgotten by any human like password as these characteristics comes along with any person. All the biometric modality has its own difficulties, merits and demerits. Hence no one modality can be used for best results. Now-a-days, every authentication system uses more than one biometric trait where one trait is fused with another one for better authentication. newline |
Pagination: | |
URI: | http://hdl.handle.net/10603/342652 |
Appears in Departments: | Computing Sciences |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 272.89 kB | Adobe PDF | View/Open |
02_certificate.pdf | 231.54 kB | Adobe PDF | View/Open | |
03_table of contents.pdf | 407.88 kB | Adobe PDF | View/Open | |
04_list of tables.pdf | 34.28 kB | Adobe PDF | View/Open | |
05_list of figures.pdf | 222.4 kB | Adobe PDF | View/Open | |
06_acknowledgement.pdf | 253.25 kB | Adobe PDF | View/Open | |
08_chapter 1.pdf | 1.44 MB | Adobe PDF | View/Open | |
09_chapter 2.pdf | 669.96 kB | Adobe PDF | View/Open | |
10_chapter 3.pdf | 949.2 kB | Adobe PDF | View/Open | |
11_chapter 4.pdf | 1.57 MB | Adobe PDF | View/Open | |
12_chapter 5.pdf | 1.13 MB | Adobe PDF | View/Open | |
13_chapter 6.pdf | 39.64 kB | Adobe PDF | View/Open | |
14_references.pdf | 148.58 kB | Adobe PDF | View/Open | |
15_publications.pdf | 249.07 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 297.17 kB | Adobe PDF | View/Open |
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