A novel 3D face feature based on geometry image vertical shape information

Optik ◽  
2015 ◽  
Vol 126 (9-10) ◽  
pp. 898-902 ◽  
Author(s):  
Hongyan Zou ◽  
Feipeng Da ◽  
Zhaoyang Wang
Keyword(s):  
Shape Information ◽  
3D Face ◽  
Feature Based ◽  
Geometry Image

scholarly journals Contribution of shape and surface reflectance information to kinship detection in 3D face images

2019 ◽  
Author(s):  
Vanessa Fasolt ◽  
Iris Jasmin Holzleitner ◽  
Anthony J Lee ◽  
Kieran J. O'Shea ◽  
Lisa Marie DeBruine
Keyword(s):  
Mixed Models ◽  
Surface Reflectance ◽  
Skin Texture ◽  
Shape Information ◽  
3D Face ◽  
Face Shape ◽  
Face Images ◽  
Facial Information ◽  
Facial Images

Previous research has established that humans are able to detect kinship among strangers from facial images alone. The current study investigated what facial information is used for making those kinship judgments, specifically the contribution of face shape and surface reflectance information (e.g., skin texture, tone, eye and eyebrow colour). Using 3D facial images, 195 participants were asked to judge the relatedness of one hundred child pairs, half of which were related and half of which were unrelated. Participants were randomly assigned to judge one of three stimulus versions: face images with both surface reflectance and shape information present (reflectance and shape version), face images with shape information removed but surface reflectance present (reflectance version) or face images with surface reflectance information removed but shape present (shape version). Using binomial logistic mixed models, we found that participants were able to detect relatedness at levels above chance for all three stimulus versions. Overall, both individual shape and surface reflectance information contribute to kinship detection, and both cues are optimally combined when presented together.

A New Parameterised Feature-Based Generic 3D Human Face Model for Emotional Bio-Robots

2013 ◽  
Vol 461 ◽  
pp. 838-847
Author(s):  
Xu Zhang ◽  
Shu Jun Zhang ◽  
Kevin Hapeshi
Keyword(s):  
Facial Expressions ◽  
Computer Game ◽  
Human Face ◽  
Human Beings ◽  
Freeform Surfaces ◽  
Face Model ◽  
3D Face ◽  
Potential Applications ◽  
Feature Based ◽  
Human Faces

To represent various human facial expressions is an essential requirement for emotional bio-robots. The human expressions can convey certain emotions for communications of human beings with some muscles positions and their movements. To design and develop emotional robots, it is necessary to build a generic 3D human face model. While the geometrical features of human faces are freeform surfaces with complex properties, it is the fundamental requirement for the model to have the ability of representing both primitive and freeform surfaces. This requirement makes the Non-rational Uniform B-Spline (NURBS) are suitable for 3D human face modelling. In this paper, a new parameterised feature based generic 3D human face model is proposed and implemented. Based on observation of human face anatomy, the authors define thirty-four NURBS curve features and twenty-one NURBS surface features to represent the human facial components, such as eyebrows, eyes, nose and mouth etc. These curve models and surface models can be used to simulate different facial expressions by manipulating the control points of those NURBS features. Unlike the existing individual based face modelling methods, this parameterised 3D face model also gives users the ability to use the model imitate any face appearances. In addition the potential applications of the new proposed 3D face model are also discussed. Besides emotional bio-robots, it is believed that the proposed model can also be applied in other fields such as aesthetic plastic surgery simulation, film and computer game characters creation, and criminal investigation and prevention.

scholarly journals Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Gurman Gill ◽  
Matthew Toews ◽  
Reinhard R. Beichel
Keyword(s):  
A Priori ◽  
Ct Scans ◽  
Shape Information ◽  
Shape Model ◽  
Lung Segmentation ◽  
Active Shape Models ◽  
Novel Approach ◽  
Segmentation Methods ◽  
Active Shape ◽  
Feature Based

Model-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process but suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach for initializing an active shape model (ASM) and apply it to 3D lung segmentation in CT scans. Our method constructs an atlas consisting of a set of representative lung features and an average lung shape. The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features. Our evaluation on a diverse set of 190 images showed an average dice coefficient of 0.746 ± 0.068 for initialization and 0.974 ± 0.017 for subsequent segmentation, based on an independent reference standard. The mean absolute surface distance error was 0.948 ± 1.537 mm. The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches. The proposed initialization method can be generalized to other applications employing ASM-based segmentation.

Symmetric surface-feature based 3D face recognition for partial data

2011 ◽  
Author(s):  
Dirk Smeets ◽  
Johannes Keustermans ◽  
Jeroen Hermans ◽  
Peter Claes ◽  
Dirk Vandermeulen ◽  
...  
Keyword(s):  
Face Recognition ◽  
Surface Feature ◽  
3D Face Recognition ◽  
3D Face ◽  
Partial Data ◽  
Feature Based ◽  
Symmetric Surface

Local Feature Based 3D Face Recognition

2005 ◽  
pp. 909-918 ◽  
Author(s):  
Yonguk Lee ◽  
Hwanjong Song ◽  
Ukil Yang ◽  
Hyungchul Shin ◽  
Kwanghoon Sohn
Keyword(s):  
Face Recognition ◽  
Local Feature ◽  
3D Face Recognition ◽  
3D Face ◽  
Feature Based

scholarly journals Automatic Object-Detection of School Building Elements in Visual Data: A Gray-Level Histogram Statistical Feature-Based Method

2019 ◽  
Vol 9 (18) ◽  
pp. 3915
Author(s):  
Zhenyu Zhang ◽  
Hsi-Hsien Wei ◽  
Sang Guk Yum ◽  
Jieh-Haur Chen
Keyword(s):  
Data Collection ◽  
Object Detection ◽  
Detection Method ◽  
Gray Level ◽  
Structural Elements ◽  
Structural Element ◽  
Shape Information ◽  
Statistical Feature ◽  
Feature Based ◽  
Gray Level Histogram

Automatic object-detection technique can improve the efficiency of building data collection for semi-empirical methods to assess the seismic vulnerability of buildings at a regional scale. However, current structural element detection methods rely on color, texture and/or shape information of the object to be detected and are less flexible and reliable to detect columns or walls with unknown surface materials or deformed shapes in images. To overcome these limitations, this paper presents an innovative gray-level histogram (GLH) statistical feature-based object-detection method for automatically identifying structural elements, including columns and walls, in an image. This method starts with converting an RGB image (i.e. the image colors being a mix of red, green and blue light) into a grayscale image, followed by detecting vertical boundary lines using the Prewitt operator and the Hough transform. The detected lines divide the image into several sub-regions. Then, three GLH statistical parameters (variance, skewness, and kurtosis) of each sub-region are calculated. Finally, a column or a wall in a sub-region is recognized if these features of the sub-region satisfy the predefined criteria. This method was validated by testing the detection precision and recall for column and wall images. The results indicated the high accuracy of the proposed method in detecting structural elements with various surface treatments or deflected shapes. The proposed structural element detection method can be extended to detecting more structural characteristics and retrieving structural deficiencies from digital images in the future, promoting the automation in building data collection.

Sign in / Sign up

Export Citation Format

Share Document