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Online fingerprint recognition / Khairul Bariyah Abd Rahim

By:

Khairul Bariyah Abd Rahim

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2010Description: xv, 61 p. : ill. (some col.) ; 30 cm. + 1 computer discISBN:

THE0007059(Local)

Other title:

PC based control smart home system using Zigbee wireless technology [computer file]

Subject(s):

Dissertation note: Project paper (Bachelor of Electrical Engineering (Electronics)) -- Universiti Malaysia Pahang - 2010 Abstract: Fingerprints are the most popularly used in biometric identification and recognition systems, because they can be easily used and their features are highly reliable. Because of their uniqueness and consistency over time, fingerprint has been used for identification for over a century, more recently becoming automated due to advancements in computing capabilities. The systems are increasingly employed into business, trading and living fields for automatic personal identification. Besides that, fingerprint recognition beyond criminal identification applications to several civilian applications such as access control, time and attendance, and computer user login. This project introduces and implementation of an online fingerprint recognition system which is capable of verifying identities of people so fast, accurate and suitable for the real time. Such a system has great utility in a variety of personal identification and access control applications by operating in minutiae extraction and minutiae matching. Minutiae extraction algorithm is implemented for extracting features from an input fingerprint image captured with an online inkless scanner. For minutiae matching, the matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and store template. The system will be tested on set of fingerprint images captured with inkless scanner. The recognition accuracy is found to be acceptable. This result shows that our systems meet the response requirement of online recognition with high accuracy. All the systems will be built using MATLAB software

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Holdings ( 2 )
Title notes ( 3 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB PEKAN	TK7882.P3 K43 2010 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000058418
Final Year Report	UMPLIB PEKAN	CD 5385 \| TK7882.P3 K43 2010 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000058419

Project paper (Bachelor of Electrical Engineering (Electronics)) -- Universiti Malaysia Pahang - 2010

Bibliography : p. 51-54

Fingerprints are the most popularly used in biometric identification and recognition systems, because they can be easily used and their features are highly reliable. Because of their uniqueness and consistency over time, fingerprint has been used for identification for over a century, more recently becoming automated due to advancements in computing capabilities. The systems are increasingly employed into business, trading and living fields for automatic personal identification. Besides that, fingerprint recognition beyond criminal identification applications to several civilian applications such as access control, time and attendance, and computer user login. This project introduces and implementation of an online fingerprint recognition system which is capable of verifying identities of people so fast, accurate and suitable for the real time. Such a system has great utility in a variety of personal identification and access control applications by operating in minutiae extraction and minutiae matching. Minutiae extraction algorithm is implemented for extracting features from an input fingerprint image captured with an online inkless scanner. For minutiae matching, the matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and store template. The system will be tested on set of fingerprint images captured with inkless scanner. The recognition accuracy is found to be acceptable. This result shows that our systems meet the response requirement of online recognition with high accuracy. All the systems will be built using MATLAB software