Personalized 3D-Aided 2D Facial Landmark Localization
نویسندگان
چکیده
Facial landmark detection in images obtained under varying acquisition conditions is a challenging problem. In this paper, we present a personalized landmark localization method that leverages information available from 2D/3D gallery data. To realize a robust correspondence between gallery and probe key points, we present several innovative solutions, including: (i) a hierarchical DAISY descriptor that encodes larger contextual information, (ii) a Data-Driven Sample Consensus (DDSAC) algorithm that leverages the image information to reduce the number of required iterations for robust transform estimation, and (iii) a 2D/3D gallery pre-processing step to build personalized landmark metadata (i.e., local descriptors and a 3D landmark model). We validate our approach on the Multi-PIE and UHDB14 databases, and by comparing our results with those obtained using two existing methods.
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