(untitled) (Journal of the College of Basic Education, 2022)
Person’s identification or recognition is a first problem step in all security systems. In these systems, Ear biometric was suggested to be a crucial tool for recognizing people in critical, important organizations and government security agencies. It is unique to each individual and relatively unchanging during lifetime. The proposed system in this study presents a new algorithm for ear recognition. It offers a mathematical model based on centroied and related cluster analysis which tries to find a reciprocal twin sample to the nearest neighbors and then they are grouped as a cluster. System is trained on individual subjects with the same conditions. Each enrolled image subject is converted to a gray scale. Experimental results reflect that the proposed system can be offered 93% accuracy, less complexity and can make more accuracy with subjects who are those have a short hair.