3D facial landmark localization for cephalometric analysis

Cephalometric analysis is an important and routine task in the medical field to assess craniofacial development and to diagnose cranial deformities and midline facial abnormalities. The advance of 3D digital techniques potentiated the development of 3D cephalometry, which includes the localization of cephalometric landmarks in the 3D models. However, manual labeling is still applied, being a tedious and time-consuming task, highly prone to intra/inter-observer variability. In this paper, a framework to automatically locate cephalometric landmarks in 3D facial models is presented. The landmark detector is divided into two stages: (i) creation of 2D maps representative of the 3D model; and (ii) landmarks' detection through a regression convolutional neural network (CNN). In the first step, the 3D facial model is transformed to 2D maps retrieved from 3D shape descriptors. In the second stage, a CNN is used to estimate a probability map for each landmark using the 2D representations as input. The detection method was evaluated in three different datasets of 3D facial models, namely the Texas 3DFR, the BU3DFE, and the Bosphorus databases. An average distance error of 2.3, 3.0, and 3.2 mm were obtained for the landmarks evaluated on each dataset. The obtained results demonstrated the accuracy of the method in different 3D facial datasets with a performance competitive to the state-of-the-art methods, allowing to prove its versability to different 3D models. Clinical Relevance - Overall, the performance of the landmark detector demonstrated its potential to be used for 3D cephalometric analysis.FCT - Fundação para a Ciência e a Tecnologia(LASI-LA/P/0104/2020

Deep, dense and accurate 3D face correspondence for generating population specific deformable models

© 2017 Elsevier Ltd We present a multilinear algorithm to automatically establish dense point-to-point correspondence over an arbitrarily large number of population specific 3D faces across identities, facial expressions and poses. The algorithm is initialized with a subset of anthropometric landmarks detected by our proposed Deep Landmark Identification Network which is trained on synthetic images. The landmarks are used to segment the 3D face into Voronoi regions by evolving geodesic level set curves. Exploiting the intrinsic features of these regions, we extract discriminative keypoints on the facial manifold to elastically match the regions across faces for establishing dense correspondence. Finally, we generate a Region based 3D Deformable Model which is fitted to unseen faces to transfer the correspondences. We evaluate our algorithm on the tasks of facial landmark detection and recognition using two benchmark datasets. Comparison with thirteen state-of-the-art techniques shows the efficacy of our algorithm