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Enhancements in Fingerprint Authentication

BROWSE_DETAIL_CREATION_DATE: 19-07-2017

BROWSE_DETAIL_IDENTIFIER_SECTION

BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: Masters

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: TECHNOLOGY,

BROWSE_DETAIL_CREATORS: Al-Subaihawi, Mohamed Abdulraheem Taqı (Author),

BROWSE_DETAIL_CONTRIBUTERS: Bostan, Atilla (Advisor),

BROWSE_DETAIL_TAB_KEYWORDS

Fingerprint identification and verification, Minutia features, ridgefeatures, Local feature approach, Global feature approach, Invariance Moment function, Region Of Interest (ROI)


BROWSE_DETAIL_TAB_ABSTRACT

Fingerprint identification and verification tasks are among the most challenging tasks in image processing and machine learning domains. Fingerprint processing presents a key issue in the biometric technologies and information security. According to the fraction of the people population based on the complete detection of the biometric fingerprint feature such as ridge structure, incomplete (portion) fingerprint image identification and verification task is very difficult to be accomplished. The main challenge in this problem is that the partial loss of the ridge structure in the incomplete fingerprint image.In this thesis, we studied the effectiveness of global feature approach in fingerprint identification and verification task that can deal with the partial image loss or incomplete fingerprint image. Global feature vector extraction is the main global approach that we contribute in this thesis. In this case, we implemented global geometrics based feature extraction for fingerprint identification and verification task. A set of global features (seven-moment values) were extracted from the partial fingerprint (incomplete fingerprint image). The study shows that global feature vector can more efficiently deal with incomplete fingerprint recognition problem when compared with the classical approach to the fingerprint identification and verification problem which is based on extracting minutia features from the fingerprint rides as well as the pores in different feature extraction levels.ivThe studied system has been tested using a database that was randomlygenerated out of some random incomplete fingerprint images. Randomly generatedincomplete fingerprint images were sorted into 10 groups according to the size of themissing part in each image. Then we randomly selected random images from eachgroup to compose a new challenge dataset to be tested in two different approacheswhich are global, and Local feature extraction approaches.The experimental results show that global approach has about 87% while thelocal approach has 17% of identification and verification effectiveness. This meansglobal approach improves the performance of the fingerprint identification andverification system on partial (incomplete) fingerprint images by 70% more than theclassical approach.


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