The relevance of this work is caused by need of high-fidelity non-rigid registration
algorithm for facial scans. Despite the fact that over the past decade, a large number of various
algorithms for 3d model registration were proposed, including models of a human face, the vast
majority of them are unable to provide accurate face registration for lips and eyes. This work
is intended to improve this situation by using a detector of facial boundaries based on deep
learning, namely using convolutional neural networks. Usage of facial landmarks detectors
(deep learning based or other algorithms) is not novel in the field of non-rigid registration.
However such approach is not good enough for precise registration. Facial landmark
annotation is ill posed problem in general case, due to ambiguity of landmarks position. For
example, it is very difficult to put landmarks in the middle of the eye so that they are
anatomically in the same place in different frames. In this work we propose an alternative
method - facial edges detection for registration. Obtained results show the advantage of the
proposed approach.
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Klimov Kirill Vitalievich
Email: ra22341@ya.ru
Voronezh State University
Voronezh, Russian Federation
