Biometrics techniques are the standard of a wide group of many applications for a human’s identification and verification issues. Because of this reason, a large scale of security needs to search for a new way to identify the person arises. In this paper, a machine learning approach for a human ear recognition system is proposed. This system combines four main phases: ear detection, ear feature extraction, ear recognition, and confirmation. The proposed system’s essential is to divide the ear image into the skin and non-skin pixels using a likelihood skin detector. The likelihood image processes by morphological operations to complete ear regions. Scale-invariant feature transform (SIFT) uses for extracting the fixed features of the ear. Ear recognition includes two modes identification mode and verification mode. Euclidean Distance Measure (EDM) uses for similarity measure between the first image in the database and a new image. According to the three experiments conducted in this paper, the results of the different datasets, the accuracy ratio are 100%, 92%.and 92%, respectively.

Biometrics techniques are the standard of a wide group of many applications for a human’s identification and verification issues. Because of this reason, a large scale of security needs to search for a new way to identify the person arises. In this paper, a machine learning approach for a human ear ...