This paper proposed the use of multi-algorithm feature-level fusion as a means to improve the performance of finger-knuckle-print (FKP) verification. LG, LPQ, PCA, and LPP have been used to extract the FKP features. Experiments are performed using the FKP database, which consists of 7,920 images. Results indicate that the multi-algorithm verification approach outperforms higher performance than using any single algorithm. The biometric performance using feature-level fusions under different normalization techniques as well has been demonstrated in this paper. © 2014 Springer India.
CITATION STYLE
Almahafzah, H., Sheshadri, H. S., & Imran, M. (2014). Multi-algorithm decision-level fusion using finger-knuckle-print biometric. In Lecture Notes in Electrical Engineering (Vol. 248 LNEE, pp. 39–47). Springer Verlag. https://doi.org/10.1007/978-81-322-1157-0_5
Mendeley helps you to discover research relevant for your work.