AI Article Synopsis
- The paper discusses a new method for pinpointing facial feature points, crucial for 3D human face modeling in medical applications.
- It introduces a technique that uses relative angle histograms in conjunction with multiscale constraints to enhance accuracy.
- Experimental findings indicate that this new approach outperforms existing methods that rely solely on relative angle histograms for localization.
Article 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4620036 | PMC |
| http://dx.doi.org/10.1155/2015/178102 | DOI Listing |
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