3-Level Techniques Comparison based Image Recognition

Abstract

Image recognition is one of the most important applications of information processing, in this paper; a comparison between 3-level techniques based image recognition has been achieved, using discrete wavelet (DWT) and stationary wavelet transforms (SWT), stationary-stationary-stationary (sss), stationary-stationary-wavelet (ssw), stationary-waveletstationary (sws), stationary wavelet-wavelet (sww), waveletstationary-stationary (wss), wavelet-stationary-wavelet (wsw), wavelet-wavelet-stationary (wws) and wavelet-waveletwavelet (www). A comparison between these techniques has been implemented. according to the peak signal to noise ratio (PSNR), root mean square error (RMSE), compression ratio (CR) and the coding noise e (n) of each third level. The two techniques that have the best results which are (sww and www) are chosen, then image recognition is applied to these two techniques using Euclidean distance and Manhattan distance and a comparison between them has been implemented., it is concluded that, sww technique is better than www technique in image recognition because it has a higher match performance (100%) for Euclidean distance and Manhattan distance than that in www..

Cryptography based on retina information

“…The retina is one of the wellsprings of biometric systems which [24] has inherent robustness [22] and give an efficient and reliable method for authentication [24], [25]. The feature extraction of the retina vessels is computing feature vectors to get a compact representation of the original retina [12], [26], [27]. In a retina feature extraction, the retina center's coordinates are calculated by drawing the  ISSN: 2502-4752 diagonals of the retina image; the enter section point of the diagonals is the center of the retina image, which is also the center of the retina.…”