Investigation And Development Of Flattening Algorithms For Curved Latent Fingerprint Images

Chong, Bei Wei (2015) Investigation And Development Of Flattening Algorithms For Curved Latent Fingerprint Images. Masters thesis, Universiti Sains Malaysia.

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Fingerprint had been used to identify a person due to its uniqueness and unchangeable throughout life. However, latent fingerprint acquisition normally being performed on uneven or noisy surface with poor contrast, causing fingerprint minutiae point extracted appear to be inaccurate and affect the result of fingerprint matching. Thus, latent fingerprint required image to be pre-process and enhance before latent search. In order to increase latent matching accuracy, geometry rectification is needed to correct distortion in fingerprint images due to uneven surfaces. This research will investigate and develop flattening algorithm that can be adapted to latent fingerprint images on cylindrical surface. The boundary of an image is required to detect the curvature of an image that need to be flattened. Boundary of interested area can be acquired using a predefined algorithm or define by user using interactive drawing. The flattening algorithm required mapping from cylindrical coordinate to image coordinate. Since curved image appears to be rectangular shape, parabolic approximation and ellipse approximation are being used to design algorithms for flattening. Experimental results prove that algorithm that applies ellipse equation to flatten fingerprint images able to increase the quality of the minutiae. However, measurement results for horizontal axes shows that the distortion in horizontal axis is not being well taken care of. In summary, both algorithms developed able to flatten curved latent fingerprint images with the assumption that image that needs to be flattened is vertical cylindrical shape and boundary of cylinder must be detectable. Algorithm that applies ellipse approximation provides better performance as compared with the algorithm that developed based on parabolic approximation.

Item Type: Thesis (Masters)
Additional Information: Accession No:875000356
Subjects: T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering > TK7800-8360 Electronics
Divisions: Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Thesis
Depositing User: Mr Mohd Fadli Abd Rahman
Date Deposited: 31 Jul 2018 07:09
Last Modified: 31 Jul 2018 07:09

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