Abstract
The aim of this research is to analyze humans fingerprint texture in order to determine their Age & Gender, and correlation of RTVTR and Ridge Count on gender detection. The study is to analyze the effectiveness of physical biometrics (thumbprint) in order to determine age and gender in humans. An application system was designed to capture the finger prints of sampled population through a fingerprint scanner device interfaced to the computer system via Universal Serial Bus (USB), and stored in Microsoft SQL Server database, while back-propagation neural network will be used to train the stored fingerprint. The specific Objectives of this research are to: Use fingerprint sensor to collect different individual fingerprint, alongside their age and gender, Formulate a model and develop a fingerprint based identification system to determine age and gender of individuals and evaluate the developed system.
Highlights
A Fingerprint is the representation of the epidermis of a finger; it consists of a pattern of interleaved ridges and valleys
Fingerprint identification and classification has been extensively researched in times past, very few researchers have studied the fingerprint gender classification problem, (Acree 1999) used the ridge density, which he defined as the number of ridges in a certain space; it was shown that the females have higher ridge density (Acree 1999). (Kralik 2003) showed that the males have higher ridge breadth,) which was defined as the distance between the centers of two adjacent valleys), than females
The technical approach followed in processing input images, detecting graphic symbols, analyzing and mapping the fingerprint and training the network for a set of desired corresponding image to the input images have been discussed
Summary
A Fingerprint is the representation of the epidermis of a finger; it consists of a pattern of interleaved ridges and valleys. Like everything in the human body, fingerprint ridges form through a combination of genetic and environmental factors. This is the reason why even the fingerprint of identical twins is different (Maltoni and Cappelli, 2006). Biometrics measures the unique physical or behavioral characteristics of individual as a means to recognize or authenticate their identity. Common physical biometrics includes fingerprints, hand or palm geometry, and retina, iris or facial characteristics. Behavioral characteristics include signature, voice (which has a physical component), keystroke pattern and gait. Some technologies have gained access control and biometrics as a whole shows great potential for use in end user segments, such as airports, stadiums, defense installations and the industry and corporate workplaces where security and privacy are required (Jain et al 2003)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Research in Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
