Rapid 3D Ear Indexing and Recognition

In this chapter, we proposes a novel method that combines the feature embedding for the fast retrieval of surface descriptors, novel similarity measures for correspondences, and a support vector machine (SVM)- based learning technique for ranking the hypotheses. The local surface patch (LSP) representation is used to find the correspondence between a model-test pair. Due to its high dimensionality, an embedding algorithm is used that maps the feature vectors to a low-dimensional space where distance relationships are preserved. By searching the nearest neighbors in low dimensions, the similarity between a model-test pair is computed using the novel features. The similarities for all modeltest pairs are ranked using the learning algorithm to generate a short list of candidate models for verification. The verification is performed by aligning a model with the test object.