Hand Geometry and Palmprint Classification System Based on Statistical Analysis

  • sarah Jasim Mohammed Mechanical Engineering Department, University of Technology, Baghdad-Iraq.
Keywords: Biometric hand geometry, palm texture feature, Principal Component Analysis

Abstract

Biometric system is considered of an important type of security systems nowadays, because it relays on the individual traits (physical or behavioral) non-participation between any two people that can't be lost on lifetime and can't be stolen. This paper, will present the individual classification system based on hand geometry and palm texture feature where it is one of the parts of the human body, which has an impressive set of information capable to distinguish and identify individuals. Utilize Principal Component Analysis PCA for palimprint texture feature extraction. The proposed system consists of three phases: image preprocessing, hand feature extraction and pattern classification. Utilize Principal Component Analysis PCA for palimprint texture feature extraction. The proposed system utilized complete hand image inside database consists of 600 pictures, include 100 people, each one has six images. Experimental results show that 98.3% is achieved and that illustrate the applicability of the system in the security's average of different environments.



Published
2017-12-01
How to Cite
Mohammed, sarah J. (2017). Hand Geometry and Palmprint Classification System Based on Statistical Analysis. Al-Nahrain Journal of Science, 20(4), 109-116. Retrieved from https://anjs.edu.iq/index.php/anjs/article/view/138
Section
Articles