16 January 2018 Ear recognition via sparse coding of local features
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Abstract
An efficient scheme for human ear recognition is presented. This scheme comprises three main phases. First, the ear image is decomposed into a pyramid of progressively downgraded images, which allows the local patterns of the ear to be captured. Second, histograms of local features are extracted from each image in the pyramid and then concatenated to shape one single descriptor of the image. Third, the procedure is finalized by using decision making based on sparse coding. Experiments conducted on two datasets, composed of 125 and 221 subjects, respectively, have demonstrated the efficiency of the proposed strategy as compared to various existing methods. For instance, scores of 96.27% and 96.93% have been obtained for the datasets, respectively.
© 2018 SPIE and IS&T
Mohamad Mahmoud Al Rahhal, Mohamad Mahmoud Al Rahhal, Mohamed Lamine Mekhalfi, Mohamed Lamine Mekhalfi, Taghreed Abdullah Mohammed Ali, Taghreed Abdullah Mohammed Ali, Yakoub Bazi, Yakoub Bazi, Mansour Al Zuair, Mansour Al Zuair, Lalitha Rangarajan, Lalitha Rangarajan, } "Ear recognition via sparse coding of local features," Journal of Electronic Imaging 27(1), 013007 (16 January 2018). https://doi.org/10.1117/1.JEI.27.1.013007 . Submission: Received: 13 September 2017; Accepted: 15 December 2017
Received: 13 September 2017; Accepted: 15 December 2017; Published: 16 January 2018
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