Fast Localization of Facial Landmark Points
원문 초록
Localization of salient facial landmark points, such as eye corners or the tip of the nose, is still considered a challenging computer vision problem despite recent efforts. This is especially evident in unconstrained environments, i.e., in the presence of background clutter and large head pose variations. Most methods that achieve state-of-the-art accuracy are slow, and, thus, have limited applications. We describe a method that can accurately estimate the positions of relevant facial landmarks in real-time even on hardware with limited processing power, such as mobile devices. This is achieved with a sequence of estimators based on ensembles of regression trees. The trees use simple pixel intensity comparisons in their internal nodes and this makes them able to process image regions very fast. We test the developed system on several publicly available datasets and analyse its processing speed on various devices. Experimental results show that our method has practical value.
함께 살펴볼 논문
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MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution
Precise Facial Landmark Detection by Dynamic Semantic Aggregation Transformer
POPoS: Improving Efficient and Robust Facial Landmark Detection with Parallel Optimal Position Search
FaceXFormer: A Unified Transformer for Facial Analysis
STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection
KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration
토론
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