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The ubiquity of smartphones combined with the developing trend toward smart living will make biometrics an important element to incorporate into homes of the future. Smartphones already use fingerprints, iris scans, and face recognition in this way. Other biometrics that are likely to come into use include retinas, veins, and palm prints. The ear is another potential biometric, and ear recognition technology, or “earprints,” could one day be used as person identification to secure smart homes via smartphones.

In addition to being a characteristic unique to an individual, a biometric should be universal and permanent, and the ear meets those criteria. Previous research has shown that even identical twins have differences in their ears, and ear structure is more or less permanent, showing few changes throughout a lifetime.

By proposing to incorporate the earprint biometric into a smartphone app, the authors from University de Tunis El Manar hope to overcome barriers such as prohibitive cost and complex installation. They envision their system as affordable, scalable to new devices, and familiar to most users. 

The authors believe the ear to be preferable to other biometrics because it is a “passive biometric.” Unlike facial recognition techniques, its effectiveness is not compromised by facial hair and expressions of emotion. Also, the ear’s color distribution is more even than in an iris or a retina. 

The researchers’ approach combines frequency domain and the local features. Through a series of estimations and extractions, ear features are fused into an identifying feature. They evaluated the research using two image databases and support vector machine, K-nearest neighbor (KNN), and random forest classifiers. They recorded best accuracies of 93.88% and 92.5%, respectively, with the KNN classifier.

The performance of previously existing ear recognition methods does not hold up in under varying lighting contrast, changing viewpoint, and inadequate image registration. The results of this research show a significant performance improvement over current techniques. 

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