Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boosted Classifiers

2006 ◽  
Author(s):  
D. Vukadinovic ◽  
M. Pantic
Keyword(s):  
Facial Feature ◽  
Feature Point ◽  
Gabor Feature ◽  
Feature Based ◽  
Fully Automatic ◽  
Point Detection ◽  
Boosted Classifiers

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 Facial feature point detection: A comprehensive survey

2018 ◽  
Vol 275 ◽  
pp. 50-65 ◽  
Author(s):  
Nannan Wang ◽  
Xinbo Gao ◽  
Dacheng Tao ◽  
Heng Yang ◽  
Xuelong Li
Keyword(s):  
Facial Feature ◽  
Feature Point ◽  
Comprehensive Survey ◽  
Point Detection

scholarly journals An optimization of facial feature point detection program by using several types of convolutional neural network

2019 ◽  
Vol 16 (2) ◽  
pp. 827
Author(s):  
Shyota Shindo ◽  
Takaaki Goto ◽  
Tadaaki Kirishima ◽  
Kensei Tsuchida
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Facial Feature ◽  
Feature Point ◽  
Detection Accuracy ◽  
Local Point ◽  
The Face ◽  
Complicated Model ◽  
Time To Learn ◽  
Point Detection

<p>Detection of facial feature points is an important technique used for biometric authentication and facial expression estimation. A facial feature point is a local point indicating both ends of the eye, holes of the nose, and end points of the mouth in the face image. Many researches on face feature point detection have been done so far, but the accuracy of facial organ point detection is improving by the approach using<br />Convolutional Neural Network (CNN). However, CNN not only takes time to learn but also the neural network becomes a complicated model, so it is necessary to improve learning time and detection accuracy. In this research, the improvement of the detection accuracy of the learning speed is improved by increasing the convolution layer.</p>

scholarly journals A Robust Shape Reconstruction Method for Facial Feature Point Detection

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Shuqiu Tan ◽  
Dongyi Chen ◽  
Chenggang Guo ◽  
Zhiqi Huang
Keyword(s):  
Feature Space ◽  
Facial Feature ◽  
Shape Space ◽  
Shape Reconstruction ◽  
Feature Point ◽  
Reconstruction Method ◽  
Large Variability ◽  
The Face ◽  
Public Datasets ◽  
Point Detection

Facial feature point detection has been receiving great research advances in recent years. Numerous methods have been developed and applied in practical face analysis systems. However, it is still a quite challenging task because of the large variability in expression and gestures and the existence of occlusions in real-world photo shoot. In this paper, we present a robust sparse reconstruction method for the face alignment problems. Instead of a direct regression between the feature space and the shape space, the concept of shape increment reconstruction is introduced. Moreover, a set of coupled overcomplete dictionaries termed the shape increment dictionary and the local appearance dictionary are learned in a regressive manner to select robust features and fit shape increments. Additionally, to make the learned model more generalized, we select the best matched parameter set through extensive validation tests. Experimental results on three public datasets demonstrate that the proposed method achieves a better robustness over the state-of-the-art methods.

Enhanced retinal modeling for face recognition and facial feature point detection under complex illumination conditions

2016 ◽  
Vol 25 (4) ◽  
pp. 043028 ◽  
Author(s):  
Yong Cheng ◽  
Zuoyong Li ◽  
Liangbao Jiao ◽  
Hong Lu ◽  
Xuehong Cao
Keyword(s):  
Face Recognition ◽  
Facial Feature ◽  
Feature Point ◽  
Point Detection

Human Facial Feature Point Detection is Based on Improved ASM Method

2011 ◽  
Vol 181-182 ◽  
pp. 139-144
Author(s):  
Yan Peng Sun
Keyword(s):  
Initial Point ◽  
Facial Feature ◽  
Feature Point ◽  
Experimental Result ◽  
Active Shape Model ◽  
Method Performance ◽  
Shape Model ◽  
Improvement Algorithm ◽  
Active Shape ◽  
Point Detection

In view of the traditional Active Shape Model (ASM) method’s flaw and insufficiency, the paper proposed several improvements methods. The traditional ASM method carries on the characteristic search using of the initial point which provided using the person face examination；in the improvement algorithm, the paper has used the method of pupil orientation on the initialization and adopted improvement methods of edge restraint, auto-adapted length of stride to effectively improved the ASM method performance. The experimental result indicated that, the improvement ASM method has a bigger enhancement in the accuracy and robustness.

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