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Ear recognition using multi-scale histogram of oriented gradients

: Damer, Naser; Führer, Jan Benedikt


Tsihrintzis, George A. (Ed.); et al. ; IEEE Computer Society:
Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing. Proceedings : IIH-MSP 2012
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2012
ISBN: 978-0-7695-4712-1
International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) <8, 2012, Piraeus>
Conference Paper
Fraunhofer IGD ()
biometric; ear recognition; feature extraction; lighting

Ear recognition is a promising biometric measure, especially with the growing interest in multi-modal biometrics. Histogram of Oriented Gradients (HOG) has been effectively and efficiently used solving the problems of object detection and recognition, especially when illumination variations are present. This work presents a robust approach for ear recognition using multi-scale dense HOG features as a descriptor of 2D ear images. The multi-scale features assure to capture the different and complicated structures of ear images. Dimensionality reduction was performed to avoid feature redundancy and provide a more efficient recognition process while being prone to over-fitting. Finally, a test was performed on a large and realistic database and the results were compared to the state of the art ear recognition approaches tested on the same dataset and under the same test procedure.