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.
Ear recognition via sparse coding of local features
Mohamed Lamine Mekhalfi;
2018-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
