Abstract. Today the design of custom in-the-ear hearing aids is based on personal experience and skills and not on a systematic description of the variation of the shape of the ear canal. In this paper it is described how a dense surface point distribution model of the human ear canal is built based on a training set of laser scanned ear impressions and a sparse set of anatomical landmarks placed by an expert. The landmarks are used to warp a template mesh onto all shapes in the training set. Using the vertices from the warped meshes, a 3D point distribution model is made. The model is used for testing for gender related differences in size and shape of the ear canal. 1

Dense surface models can be used to register unseen surfaces, using an algorithm which is a hybrid of iterative closest-point (ICP) and active shape model (ASM) fitting. In this paper we give details of this procedure and show how it can be improved by sequentially extending the transform group over which it operates. We also evaluate it for robustness to the position of the target and to shape variation across a set of unseen examples. The fit was successful on all 21 examples in our test set, with an average RMS error of 3.0mm. An initial comparison of 3 people landmarking the same scans suggests that this is within the normal landmark reproducibility range for 3D face scans. 1

This thesis describes and evaluates Dense Surface Models (DSMs), a new technique for building point distribution models of surfaces, from raw input data. DSMs can be used on data from a wide range of surface acquisition systems without preprocessing since they do not require that the surfaces be closed or even locally manifold, and can cope well with holes and spikes in the surfaces. This is an advantage over comparable techniques, which impose such constraints on the input. The core of the DSM algorithm is as follows. Adense correspondence is made between the surfaces using thin-plate spline warping guided by means of a small set of hand-placed landmarks. The area of interest is automatically defined by a threshold on a measure of the closeness of the correspondence at each point. Apoint distribution model is then built using the vertices from the trimmed and densely-corresponded surfaces. The key benefit of using models of the whole surface is illustrated by the large improvement in classification on face shape that is obtained when using DSMs as compared

Abstract—An automatic method for the extraction of feature points for face based applications is proposed. The system is based upon volumetric feature descriptors, which in this paper has been extended to incorporate scale space. The method is robust to noise and has the ability to extract local and holistic features simultaneously from faces stored in a database. Extracted features are stable over a range of faces, with results indicating that in terms of intra-ID variability, the technique has the ability to outperform manual landmarking. Keywords—Scale space volume descriptor, feature extraction, 3D facial landmarking I.