Finger knuckle biometrics for personal identification using statistical and feature based approaches

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Texture pattern observed on finger knuckle joint is called as Finger Knuckle Print (FKP). FKP can be extracted from inner side or back side of finger surface. FKP is extremely unique and makes the knuckle surface an emerging biometric modality. The FKP may be useful in user identification and has attracted attention of very few researchers. Our proposed work is based on FKP of back side of finger surface. The objective of this work is to investigate and develop a systematic approach for identifying a person using his/her finger knuckle print. Three different methodologies are employed for FKP identification process. Statistical approach (Principal Component Analysis) and feature based approaches (Gabor based coding scheme and Radon like features) are used to accomplish this objective. Nearest Mean Classification, Angular distance matching and Dynamic Time Warping methods are used for finding similarity between enrolled FKP and test FKP images. Performance comparison of proposed three methodologies is done by computing different performance measures such as FAR, FRR, EER, Di and CRR. Proposed methods are producing EER 0.2 using statistical approach and 0.03 using feature based approach.

     

     

  • Keywords


    Finger Knuckle Print (FKP); Principal Component Analysis (PCA); Radon Like Features (RLF); EER (Equal Error Rate); DI (Decidability Index); CRR (Correct Recognition Rate).

  • References


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Article ID: 17193
 
DOI: 10.14419/ijet.v7i4.17193




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