Accurate automatic personal identification is becoming more and more important to the operation of our increasingly electronically interconnected information society. Among the several types of biometrics used for security purpose, finger-knuckle print biometric has a low data collection error rate and high user acceptability. So, the finger-knuckle print identification has been the important topic of research for many years. Another approaches that uses the estimated orientation field in a finger-knuckle print image to classify finger- knuckle print, on the other hand suffer from lower accuracy. The work explored the use of image processing in finger knuckle-print gives the different parameters of finger-knuckle print, and also explains in what amount two finger-knuckle print are similar. This system is used for the verification of a person, and we also analysis PSNR as well as SNR.