Ear biometrics for human classification based on region features mining

This paper presents an ear biometric approach to classify humans. Accordingly an improved local features extraction technique based on ear region features is proposed. Accordingly, ear image is segmented in to certain regions to extract eigenvector from all regions. The extracted features are normalized and fed to a trained neural network. To benchmark results, benchmark database from University of Science and Technology Beijing (USTB) is employed that have mutually exclusive sets for training and testing. Promising results are achieved that are comparable in the state of art. However, a few region features exhibited low accuracy that will be addressed in the subsequent research.