Understanding of the shape and size of different features of human body from the scanned data is necessary for automated design and evaluation of product ergonomics. In this paper, a computational framework is presented for automatic detection and recognition of several facial feature-regions from scanned head and shoulder polyhedral models. A noise tolerant methodology is proposed using discrete curvature computations and morphological tools for isolation of the primary feature regions of face namely eye, nose and mouth. Spatial disposition of the critical points of these isolated feature-regions is analyzed for recognition of these critical points as the standard landarks associated with the primary facial features. A number of clinically identified landmarks lie on the facial midline. An efficient algorithm for detection and processing of the midline using a point samplng technique is also presented. The results are matching well with human perception and measurements done manually on the subjects.

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