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As far as I understand you are trying to infer 3D position of the various points (i.e. height) on the beach using single monocular RGB camera. I will try to give the best answer that with the CV knowledge that I have. This is a difficult problem because you do not have the depth (distance from the camera) information, so you cannot infer full 3D structure ...


There is a recent survey paper on exactly this topic: Wu & Ji, Facial Landmark Detection: A Literature Survey, International Journal of Computer Vision 127:2, February 2019, pp 115-142. That should give you pointers to everything you might want to know. If you don't have access to Springer, here's the copy on arXiv.


I would expect most of the works use Generative Adversarial Networks (GANs) for this because they are powerful generative models capable of learning the complex underlying probability distribution. In this amazing work the authors used a Conditional GAN, in which they can generate an image conditioned on semantic segmentation map. In your case, you might ...


Alternatively you should consider using deep learning for this. Current state-of-the-art methods in recognition (including facial landmark detection) are based on neural networks. Several notable papers you might want to read are "OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields" and "Simple Baselines for Human Pose Estimation ...

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