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2. Master Thesis / Tesis Master
3. Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

Title:	Palmprint recognition using invariant moments based on wavelet transform
Authors:	Inass Shahadha Hussein (P63364)
Supervisor:	Md. Jan Nordin, Prof. Dr.
Keywords:	Wavelet transform
Issue Date:	2014
Description:	A biometric system is one which requires the recognition of a pattern whereby it enables the differentiation of features from one individual to another. Biometric technologies, thus can be defined as the automated methods of identifying or authenticating the identity of a living person based on a physiological or behavioral traits. This research emphasizes palmprint recognition which provides a wide deployment range of authentication methods. The palmprint contains principal lines, wrinkles, fine lines, ridges, and surface area; thus the palmprint of person differs from one to another. The previous researchers have difficulty to extract the features of a palmprint, because it is effects of rotation, translation, and scaling changes, and the accuracy rate of authentication performance need to be improved. The objective of this research to extract accurate shape features using invariant moments algorithm based on wavelet transform and finding high authentication. The proposed model in this research uses the 2D wavelet transform to decompose palmprint image into four levels and then uses an invariant moments algorithm to extract shape features by deriving seven moments from every level of palmprint image. After that using a developed method to do matching and identify both identification and verification. This model has shown a very good result without affecting of rotation, translation and scaling of objects, because it is associated with the use of a good description of shape features. This system has been tested using two databases from Indian Institute of Technology Kanpur (IITK), and Chinese Academy of Sciences (CASIA), Beijing. By using false rejection rate (FRR) and false acceptance rate (FAR), we calculate the accuracy of both (identification and verification). The experiment shows 97.33% identification rate and 97.99% verification rates in the IITK database and 98% identification rate and 98% verification rate in the CASIA database.,Master/Sarjana
Pages:	85
Publisher:	UKM, Bangi
Appears in Collections:	Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

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