Fast 3D Ear Extraction and Recognition

The main drawbacks of existing 3D ear extraction and recognition algorithms are their long processing time and low recognition rate.In this paper,a novel approach for fast 3D ear extraction and two approaches for 3D ear identification are proposed.For the ear extraction,the ear pose and position are normalized by aligning ear to the mean ear by iterative closest point using invariant features(ICPIF) algorithm.A mask is finally used to extract the 3D ear.In the first 3D ear identification approach,ear is represented by a combination of range image and curvature image.Principle component analysis is then adopted to reduce the dimensionality,followed by the nearest neighbor(NN) algorithm for ear recognition.In the second 3D ear identification approach,the ICPIF algorithm is used to align the probe ear and gallery ear.The registration error is used for ear recognition.Experimental results show that our first ear identification approach has a relatively good recognition rate but a very fast computing speed,and our second approach could achieve a very high recognition rate,but less computationally efficient compared with the first one.