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.
This work presents a region-growing image segmentation approach based on superpixel decomposition. From an initial contour-constrained oversegmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions. This approach raises two key issues: (1) how to compute the similarity between superpixels in order to perform accurate merging and (2) in which order those superpixels must be merged together. In this perspective, we first introduce a robust adaptive multiscale superpixel similarity in which region comparisons are made both at content and common border level. Second, we propose a global merging strategy to efficiently guide the region merging process. Such strategy uses an adaptive merging criterion to ensure that best region aggregations are given highest priorities. This allows the ability to reach a final segmentation into consistent regions with strong boundary adherence. We perform experiments on the BSDS500 image dataset to highlight to which extent our method compares favorably against other well-known image segmentation algorithms. The obtained results demonstrate the promising potential of the proposed approach.
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