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Fully automated image segmentation is an essential step for designing automated identification systems. In this paper, we address the problem of fully automated image segmentation in the context of ear biometrics. Our segmentation approach achieves more than 90% accuracy based on three different sets of 3750 facial images for 376 persons. We also present an approach for the automated evaluation of the quality of segmented images. Our approach is based on low computational-cost appearance-based features and learning based Bayesian classifier in order to determine whether the segmentation outcome is proper or improper segment. Experimental results for evaluating the segmentation outcomes of ear images indicate the benefits of the proposed scheme.