An improved cellular automata (ca) based image denoising method for biometric applications

Abstract

Biometric recognition plays a more important role in the industries and various plays to allow authenticated entry. The database would be created using the biometrics gathered from the users and authentication will be done by matching user biometric with the database. However this matching might increase the false positive rate in case of presence of salt and pepper noise in the images. This effort antagonizes a completely unique approach for screaming pel recognition and refurbishment of grey scale image victimization mirrored cellular automata (CA). The planned method eliminates salt and pepper noise from a tainted image. The planned technique permits extension lead of window size vigorously throughout great noise concentrations. The planned technique uses reflected CA supported Moore neighborhood (8-neighborhood cell). This image denoising method is examined with numerous prevailing image denoising methods like, Median filter, Switch Median (SM) filter, Directional- Weighted-Median (DWM) filter, changed DWM (MDWM), Fuzzy Cellular Automata (FCA), changed call primarily based asymmetrical cut Median Filter (MDBUTMF), and Contra mean value (CHM) filter. 3 sample pictures (Finger print, Iris, and Mandrill) of 2 completely diverse determinations (512 × 512) and (256 × 256) are engaged for the recital examination. The reflected CA is evaluated in contradiction of Peak signal-to-noise (PSNR), Mean square Error (MSE), and Structural SIMilarity (SSIM). It’s ascertained that reflected CA performs higher than the opposite prevailing practices by means of PSNR.