Since earprint is an encouraging physical trait that has been recently promoted as a biometric asset, we propose it as an alternative to other popular biometrics thanks to its uniqueness and stability. We propose an approach for ear recognition to smart home access in degraded conditions based on local and frequency domain features. The saliency is estimated with the dual tree complex wavelets in five scales and six rotation angles. Several statistic features are generated from the extracted feature vector and its first and second derivatives. The Harris descriptors are deployed to extract corner points invariant to scale, translation, and rotation. All the extracted features are fused at a feature level. To evaluate our research, we use the USTB-I and EVDDC databases. Different classifiers are utilized in the evaluation like the support vector machine, the K-nearest neighbor, and the random forest. The best recorded accuracy is 93.88% and 92.5%, respectively, with the KNN classifier.
You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.