Abstract
It is an important task to locate facial feature points due to the widespread application of 3D human face models in medical fields. In this paper, we propose a 3D facial feature point localization method that combines the relative angle histograms with multiscale constraints. Firstly, the relative angle histogram of each vertex in a 3D point distribution model is calculated; then the cluster set of the facial feature points is determined using the cluster algorithm. Finally, the feature points are located precisely according to multiscale integral features. The experimental results show that the feature point localization accuracy of this algorithm is better than that of the localization method using the relative angle histograms.
Highlights
With the development of 3D information acquisition technology, the research of 3D facial feature has gained more and more extensive attention
In order to solve these problems, we put forward a 3D facial feature point localization method based on the relative angle histograms and multiscale constraints
The localization method based on relative angle histograms has limitations, because it locates feature points to a smaller range through comparing the similarity of relative angle histograms and this method is suitable for conspicuous feature points, while the accuracy will be decreased for the inconspicuous feature points
Summary
It is an important task to locate facial feature points due to the widespread application of 3D human face models in medical fields. We propose a 3D facial feature point localization method that combines the relative angle histograms with multiscale constraints. The relative angle histogram of each vertex in a 3D point distribution model is calculated; the cluster set of the facial feature points is determined using the cluster algorithm. The feature points are located precisely according to multiscale integral features. The experimental results show that the feature point localization accuracy of this algorithm is better than that of the localization method using the relative angle histograms
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