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Static occupant classification is an important requirement in designing so-called "smart airbags." Systems for this purpose can be either based on pressure sensors or vision sensors. Vision-based systems are advantageous over pressure-sensor-based systems as they can provide additional functionalities like dynamic occupant-position analysis or child-seat orientation detection. The focus of this paper is to evaluate and analyze static occupant classification using a low-resolution range sensor, which is based on the time-of-flight principle. This range sensor is advantageous, since it provides directly a dense range image that is independent of the ambient illumination conditions and object textures. Herein, the realization of an occupant-classification system, using a novel low-resolution range image sensor, is described, methods for extracting robust features from the range images are investigated, and different classification methods are evaluated for classifying occupants. Bayes quadratic classifier, Gaussian mixture-model classifier, and polynomial classifier are compared to a clustering-based linear-regression classifier using a polynomial kernel. The latter one shows improved results compared to the first-three classification methods. Full-scale tests have been conducted on a wide range of realistic situations with different adults and child seats in various postures and positions. The results prove the feasibility of low-resolution range images for the current application.