Abstract
In this paper, we utilize a feedforward fuzzy neural network to solve the problem of fingerprint-images classification into four classes: Whorl, Left Loop, Right Loop and Arch. At first, each fingerprint image is reduced noise, removed the background and then binarized. Then, one of thinning processes is chosen to perform on the image. Consequently, the 6x5 directional matrix which represents the global flow shapes of the fingerprint is established. After being transformed into 6 x 5 directional matrices, the four matrices of the above classes will be utilized as training patterns for a ANN. After learning from the four training pasterns above, our trial FNN has been experimented through 53 fingerprint samples and results in the classification rate of 96.23%. The obtained testing results demonstrate the system's power of classification.
Sign-in/Register to access full text options 
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 callDisclaimer: 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.
