  This paper proposed the use of multi-instance feature level fusion as a means
to improve the performance of Finger Knuckle Print (FKP) verification. A
log-Gabor filter has been used to extract the image local orientation
information, and represent the FKP features. Experiments are performed using
the FKP database, which consists of 7,920 images. Results indicate that the
multi-instance verification approach outperforms higher performance than using
any single instance. The influence on biometric performance using feature level
fusion under different fusion rules have been demonstrated in this paper.
