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.