International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 70 - Number 9 |
Year of Publication: 2013 |
Authors: Shubhangi Neware, Kamal Mehta, A. S. Zadgaonkar |
10.5120/11990-7868 |
Shubhangi Neware, Kamal Mehta, A. S. Zadgaonkar . Finger Knuckle Identification using Principal Component Analysis and Nearest Mean Classifier. International Journal of Computer Applications. 70, 9 ( May 2013), 18-23. DOI=10.5120/11990-7868
The texture pattern produced by the finger knuckle bending is highly unique and makes the surface a distinctive biometric identifier. This paper presents literature survey and classification method for an emerging biometric identifier, namely Finger-Knuckle-Print (FKP), for personal identification. The FKP feature extraction is done using Principal Component Analysis (PCA) technique. Also Knuckle classification using nearest mean classifier is proposed in this paper. The experimental results from the proposed approach are promising and confirm the usefulness of this approach for personal identification.