Abstract
Currently, face masking is strongly recommended for people when being outside in order to prevent the COVID-19 spread. However, by doing so, the face area is significantly blocked by the mask, resulting in an ineffective accuracy for face recognition system. To be able to identify a person while wearing a face mask, an alternative system has to be considered. There have been several studies in ear recognition system in which an impressive accuracy is obtained. In this work, ear recognition system with the AMI ear database is studied. the feature in terms of histogram of oriented gradients (HOG) is used, and the support vector machine (SVM) is adopted for classification process. To increase the recognition accuracy, ear images are preprocessed by adjusting the sharpness level. It is found that using the concatenated HOG features from the sharpened RGB and HSV images, a promising average recognition accuracy of 86% and the standard deviation of 2.91% are obtained.
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