scholarly journals A Multiscale Constraints Method Localization of 3D Facial Feature Points

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Hong-an Li ◽  
Yongxin Zhang ◽  
Zhanli Li ◽  
Huilin Li

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.

2013 ◽  
Vol 303-306 ◽  
pp. 1402-1405 ◽  
Author(s):  
Chang Yuan Wang ◽  
Mei Juan Qu ◽  
Hong Bo Jia ◽  
Hong Zhe Bi

This paper proposed a new facial feature points localization algorithm based on main characteristics of eyes.Use the result of pupil center position to initialize the model of hybrid improved active shape model (ASM) and active appearance model (AAM). The algorithm will use two-dimensional local gray information to update the feature point position when using ASM to locate the face contour feature points. As to the internal features point location, it establishes facial organs independent AAM model. At the same time, it optimizes measure functions of ASM and AAM to judge the convergence of search algorithm. The experimental results show that the new algorithm greatly improved the localization accuracy of facial feature points.


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