Face landmarking, defined as the detection and localization of certain characteristic points on the face, is an important\r\nintermediary step for many subsequent face processing operations that range from biometric recognition to the\r\nunderstanding of mental states. Despite its conceptual simplicity, this computer vision problem has proven extremely\r\nchallenging due to inherent face variability as well as the multitude of confounding factors such as pose, expression,\r\nillumination and occlusions. The purpose of this survey is to give an overview of landmarking algorithms and their\r\nprogress over the last decade, categorize them and show comparative performance statistics of the state of the art.\r\nWe discuss the main trends and indicate current shortcomings with the expectation that this survey will provide\r\nfurther impetus for the much needed high-performance, real-life face landmarking operating at video rates.
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