Palmprint Verification Using Invariant Moments Based on Wavelet Transform

Abstract

Data security is one of the important issues among computer users. Data security can prevent fraudulent users from accessing an individual's personal data. The biometrics recognition as one of the most important parts in the security of the data and the application of computer vision. The biometrics is the authentication method used in a wide variety of applications such as e-banking, e-commerce, e-government and many others. 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 may be defined as the automated methods of identifying, or authenticating, the identity of a living person based on physiological or behavioral traits. This study 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. Previous researchers have difficulty extracting the features of a palm print, because of the effects of rotation, translation and scaling changes and the accuracy rate of verification performance needs to be improved. The aim of this study is to extract shape features using an invariant moments algorithm based on wavelet transform and identify the person's verification. This model has shown a promising results without the effects 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 databases from the Indian Institute of Technology, Kanpur (IITK), by using the False Rejection Rate (FRR) and False Acceptance Rate (FAR), we may calculate the accuracy rate of verification. The experiment shows a 97.99% accuracy rate of verification.

Estimated and Analysis of the Relationship Between the Endogenous and Exogenous Variables Using Fuzzy Semi-Parametric Sample Selection Model

“…These simulations could be improved. Babuska and Verbruggen (1996;Chandramohan and Kamalakkannan, 2014;Hussein and Nordin, 2014;Kareem, et al, 2014;Kahtan et al, 2014;Sridharan and Chitra, 2014) mentioned that modeling of complex systems will always remain an interactive approach. Thus, future study could consider the usage of other software packages or programming languages and incorporate graphic interface.…”

The Extract Region of Interest in High-Resolution Palmprint using 2D Image Histogram Entropy Function

“…Palmprint image can be classified into low-resolution (below 150 ppi) and highresolution (about 500 ppi). Low resolution is appropriate for commercial and civil applications while high resolution is used for forensic applications (Jain and Feng, 2009;Hussein and Nordin, 2014;Fei et al, 2018;Jamshidnezhad et al, 2012). In addition, it is important to note that any biometric system consists of four major steps: Image capture, image processing (which includes image segmentation), feature extraction and matching (Kumar, 2018;Wu et al, 2018;Zhang et al, 2018;Kahaki et al, 2016;Ashtari et al, 2015).…”

Finger Knuckle-Print Identification Based on Local and Global Feature Extraction Using Sdost

“…The biometric systems are designed for a reliable and accurate identification/verification. Biometrics can use physical characteristics like fingerprints (Ponnarasi and Rajaram, 2012), face, palmprints (Gayathri and Ramamoorthy, 2012;Hussein and Nordin, 2014), ear (Algabary et al, 2014), irises, or hand veins, or behavioural characteristics like speech, hand scripting or typing beat, to authenticate an individual. The personal traits are very difficult to lose or forget compared to the keys and passwords.…”