Anthropometric measures are used for individualizing head-related transfer functions (HRTFs) for example, by selecting best match HRTFs from a large library or by manipulating HRTF with respect to anthropometrics. Within this process, an accurate extraction of anthropometric measures is crucial as small changes may already influence the individualization. Anthropometrics can be measured in many different ways, e.g., from pictures or anthropometers. However, these approaches tend to be inaccurate. Therefore, we propose to use Kinect for generating individual 3D head-and-shoulder meshes from which anthropometrics are automatically extracted. This is achieved by identifying and measuring distances between characteristics points on the outline of each mesh and was found to yield accurate and reliable estimates of corresponding features. In our experiment, a large set of anthropometric measures was automatically extracted for 61 subjects and evaluated in terms of a cross-validation against the manually extracted correspondent.
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