Facial Feature Detection Algorithm Based on Main Characteristics of Eyes

2013 ◽  
Vol 303-306 ◽  
pp. 1402-1405 ◽  
Author(s):  
Chang Yuan Wang ◽  
Mei Juan Qu ◽  
Hong Bo Jia ◽  
Hong Zhe Bi
Keyword(s):  
Feature Detection ◽  
Search Algorithm ◽  
Facial Feature ◽  
Detection Algorithm ◽  
Appearance Model ◽  
Feature Points ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
The Face ◽  
Face Contour

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.

Active Shape and Active Apparent Hybrid Model of Human Facial Feature Points Localization Algorithm

2012 ◽  
Vol 220-223 ◽  
pp. 2284-2287
Author(s):  
Chang Yuan Wang ◽  
Jing Wang ◽  
Mei Juan Qu
Keyword(s):  
Search Algorithm ◽  
Facial Feature ◽  
Appearance Model ◽  
Feature Points ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Contour Feature ◽  
Active Shape ◽  
The Face ◽  
Face Contour

An improved active shape model (ASM) and active appearance model (AAM) based new method is proposed, this method 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 uses different measure functions to judge the convergence of search algorithm. The experimental results show that the new algorithm greatly improved the localization accuracy of facial feature points.

scholarly journals Facial Feature Model for a Portrait Video Stylization

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 442 ◽  
Author(s):  
Dongxue Liang ◽  
Kyoungju Park ◽  
Przemyslaw Krompiec
Keyword(s):  
Facial Feature ◽  
Feature Model ◽  
Facial Features ◽  
Outer Face ◽  
Flow Estimation ◽  
Photorealistic Rendering ◽  
Stroke Models ◽  
The Face ◽  
Face Contour ◽  
Human Faces

With the advent of the deep learning method, portrait video stylization has become more popular. In this paper, we present a robust method for automatically stylizing portrait videos that contain small human faces. By extending the Mask Regions with Convolutional Neural Network features (R-CNN) with a CNN branch which detects the contour landmarks of the face, we divided the input frame into three regions: the region of facial features, the region of the inner face surrounded by 36 face contour landmarks, and the region of the outer face. Besides keeping the facial features region as it is, we used two different stroke models to render the other two regions. During the non-photorealistic rendering (NPR) of the animation video, we combined the deformable strokes and optical flow estimation between adjacent frames to follow the underlying motion coherently. The experimental results demonstrated that our method could not only effectively reserve the small and distinct facial features, but also follow the underlying motion coherently.

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.

AAM Based Facial Feature Region Segmentation in Traditional Chinese Medicine Complexion Diagnosis

2013 ◽  
Vol 790 ◽  
pp. 535-538
Author(s):  
Jian Jun Wu ◽  
Jian Jun Yan ◽  
Fu Feng Li ◽  
Yi Qin Wang ◽  
Rui Guo ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Facial Feature ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Internal Organs ◽  
Region Segmentation ◽  
Face Region ◽  
Key Points ◽  
The Face

Fast and accurate segmentation of the facial feature regions is of great significance to research on objectification of complexion diagnosis. In this paper, we used the active appearance model (AAM) system to accurately locate 68 key points, and segmented the face region simultaneously. According to the Chinese medicine complexion-viscera principle, five face regions representing the five internal organs were segmented by using 68 key points. Experiments have shown that this method was efficient and fast for facial feature region segmentation, and laid a foundation for further research of objectification of complexion diagnosis.

Head Movement Recognition Technology Research in Augmented Reality Application

2014 ◽  
Vol 556-562 ◽  
pp. 2611-2618
Author(s):  
Hui Bai Wang ◽  
Lei Li
Keyword(s):  
Augmented Reality ◽  
Head Movement ◽  
Skin Color ◽  
Hot Spot ◽  
Detection Algorithm ◽  
Head Movements ◽  
Localization Algorithm ◽  
Movement Recognition ◽  
Augmented Reality Application ◽  
The Face

As human-computer interaction gradually shifting to people-centered, head movement recognition technology in Augmented Reality application is becoming a hot spot of research. This paper presents an improved fast face detection algorithm based on skin color model, to distinguish the face region and the non-face regions, uses the feature localization algorithm based on color features to locate the facial features, and defines a series of simple head movements and associates them with operations.

Accurate landmarking from 3D facial scans by CNN and cascade regression

2018 ◽  
Vol 16 (02) ◽  
pp. 1840007 ◽  
Author(s):  
Wang Kai ◽  
Jun An ◽  
Xi Zhao ◽  
Jianhua Zou
Keyword(s):  
Mapping Function ◽  
Facial Feature ◽  
Feature Points ◽  
Lighting Condition ◽  
The Face ◽  
Geometry Feature ◽  
The Mean ◽  
Scan Data ◽  
Cascade Regression ◽  
Coarse To Fine

Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other types of facial analysis. However, most of the existing works still focus on the 2D facial image which is quite sensitive to the lighting condition changes. In order to address this limitation, this paper proposed a coarse-to-fine method only based on the 3D facial scan data extracted from professional equipment to automatically and accurately estimate the landmark localization. Specifically, we firstly trained a convolutional neural network (CNN) to initialize the face landmarks instead of the mean shape. Then the proposed cascade regression networks learn the mapping function between 3D facial geometry feature and landmarks location. Tested on Bosphorus database, the experimental results demonstrated effectiveness and accuracy of the proposed method for [Formula: see text] landmarks. Compared with other methods, the results in several points demonstrate state-of-the-art performance.

scholarly journals Gray-Edge-HOG feature based cascaded learning for facial landmark detection

2018 ◽  
Vol 189 ◽  
pp. 10023
Author(s):  
Wenhui Zhang ◽  
Wentong Wang ◽  
Shuang Zhao ◽  
Bin Sun
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Facial Feature ◽  
Appearance Model ◽  
Detection Accuracy ◽  
Feature Points ◽  
Data Set ◽  
Facial Landmark Detection

Compared with the traditional statistical models, such as the active shape model and the active appearance model, the facial feature point localization method based on deep learning has improved in accuracy and speed, but there still exist some problems. First, when the traditional deep neural network model targets a data set containing different face poses, it only performs the preprocessing through the initialized face alignment, and does not consider the regularity of the distribution of the feature points corresponding to the face pose during feature extraction. Secondly, the traditional deep neural network model does not take into account the feature space differences caused by the different position distribution of the external contour points and internal organ points (such as eyes, nose and mouth), resulting in inconsistent detection accuracy and difficulty of different feature points. In order to solve the above problems this paper proposes a convolutional neural network (CNN) based on grayedge-HOG (GEH) fusion feature.

scholarly journals An Improved Three-dimensional DV-Hop Localization Algorithm Optimized by Adaptive Cuckoo Search Algorithm

2017 ◽  
Vol 13 (02) ◽  
pp. 102 ◽  
Author(s):  
Lieping Zhang ◽  
Fei Peng ◽  
Peng Cao ◽  
Wenjun Ji
Keyword(s):  
Search Algorithm ◽  
Three Dimensional ◽  
Cuckoo Search ◽  
Adaptive Strategy ◽  
Cuckoo Search Algorithm ◽  
Position Estimation ◽  
Localization Algorithm ◽  
Node Localization ◽  
Localization Accuracy ◽  
Unknown Node

Aiming at the low accuracy of DV-Hop localization algorithm in three-dimensional localization of wireless sensor network, a DV-Hop localization algorithm optimized by adaptive cuckoo search algorithm was proposed in this paper. Firstly, an improved DV-Hop algorithm was proposed, which can reduce the localization error of DV-Hop algorithm by controlling the network topology and improving the method for calculating average hop distance. Meanwhile, aiming at the slow convergence in traditional cuckoo search algorithm, the adaptive strategy was improved for the step search strategy and the bird's nest recycling strategy. And the adaptive cuckoo search algorithm was introduced to the process of node localization to optimize the unknown node position estimation. The experiment results show that compared with the improved DV-Hop algorithm and the traditional DV-Hop algorithm, the DV-Hop algorithm optimized by adaptive cuckoo search algorithm improved the localization accuracy and reduced the localization errors.

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