A Hybrid Algorithm of Facial Feature Point Location Based on Improved MR-ASM and AAM

2006 ◽  
Author(s):  
Zhang Sicong ◽  
Wu Lifang ◽  
He Xiaoguang ◽  
Tian Jie
Keyword(s):  
Hybrid Algorithm ◽  
Facial Feature ◽  
Feature Point ◽  
Point Location

Road rage detection algorithm based on fatigue driving and facial feature point location

2022 ◽  
Author(s):  
Wu Shulei ◽  
Suo Zihang ◽  
Chen Huandong ◽  
Zhao Yuchen ◽  
Zhang Yang ◽  
...  
Keyword(s):  
Facial Feature ◽  
Detection Algorithm ◽  
Feature Point ◽  
Road Rage ◽  
Point Location ◽  
Fatigue Driving

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

Accuracy and robust estimation of homography based on feature point location noise

2015 ◽  
Vol 23 (8) ◽  
pp. 2357-2368 ◽  
Author(s):  
赵春阳 ZHAO Chun-Yang ◽  
赵怀慈 ZHAO Huai-Ci
Keyword(s):  
Robust Estimation ◽  
Feature Point ◽  
Point Location

Tele-Operation of Robot using Facial Feature Point Detection

2019 ◽  
Author(s):  
Masahiko Minamoto ◽  
Hidaka Sato ◽  
Takahiro Kanno ◽  
Tetsuro Miyazaki ◽  
Toshihiro Kawase ◽  
...  
Keyword(s):  
Facial Feature ◽  
Feature Point ◽  
Point Detection

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
Keyword(s):  
Facial Feature ◽  
Feature Point ◽  
Distribution Model ◽  
Feature Points ◽  
Widespread Application ◽  
Point Distribution ◽  
Localization Accuracy ◽  
Localization Method ◽  
Relative Angle ◽  
Point Distribution Model

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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