Active Shape Models with Invariant Optimal Features (IOF-ASMs)
This paper is framed in the field of statistical face analysis. In particular, the problem of accurate segmentation of prominent features of the face in frontal view images is addressed. Our method constitutes an extension of Cootes et al.  linear Active Shape Model (ASM) approach, which has already been used in this task . The technique is built upon the development of a non-linear appearance model, incorporating a reduced set of differential invariant features as local image descriptors. These features are invariant to rigid transformations, and a subset of them is chosen by Sequential Feature Selection (SFS) for each landmark and resolution level. The new approach overcomes the unimodality and gaussianity assumptions of classical ASMs regarding the distribution of the intensity values across the training set. Validation of the method is presented against the linear ASM and its predecesor, the Optimal Features ASM (OF-ASM)  using the AR and XM2VTS databases as testbed.
KeywordsFalse Alarm Probability Segmentation Accuracy Rigid Transformation Segmentation Error Active Appearance Model
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