A Sense of Reality Human Face Modeling Research Based on Multi-angle Photograph

2010 ◽  
Author(s):  
Li Yahui
Keyword(s):  
Human Face ◽  
Face Modeling ◽  
Sense Of Reality

scholarly journals A Practical Approach for Identity-Embodied 3D Artistic Face Modeling

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Tanasai Sucontphunt
Keyword(s):  
Real Time ◽  
Gpu Acceleration ◽  
Human Face ◽  
Human Identity ◽  
Face Modeling ◽  
Novice User ◽  
Face Model ◽  
The Face ◽  
Core Idea ◽  
Cartoon Characters

This paper describes a practical technique for 3D artistic face modeling where a human identity can be inserted into a 3D artistic face. This approach can automatically extract the human identity from a 3D human face model and then transfer it to a 3D artistic face model in a controllable manner. Its core idea is to construct a face geometry space and a face texture space based on a precollected 3D face dataset. Then, these spaces are used to extract and blend the face models together based on their facial identities and styles. This approach can enable a novice user to interactively generate various artistic faces quickly using a slider control. Also, it can run in real-time on an off-the-shelf computer without GPU acceleration. This approach can be broadly used in various 3D artistic face modeling applications such as a rapid creation of a cartoon crowd with different cartoon characters.

Fast 3D Human Face Modeling Method Based on Multiple View 2D Images

2013 ◽  
Vol 273 ◽  
pp. 796-799
Author(s):  
Yong Sheng Wang
Keyword(s):  
Gabor Filter ◽  
Visual Features ◽  
Translation Vector ◽  
Human Face ◽  
Face Modeling ◽  
3D Images ◽  
Multiple View ◽  
Novel Approach ◽  
2D And 3D ◽  
2D Images

This paper presents a novel approach to model 3D human face from multiple view 2D images in a fast mode. Our proposed method mainly includes three steps: 1) Face Recognition from 2D images, 2) Converting 2D images to 3D images, 3) Modeling 3D human face. To extract visual features of both 2D and 3D images, visual features adopted in 3D are described by Point Signature, and visual features utilized in 2D is represented by Gabor filter responses. Afterwards, 3D model is obtained by combining multiple view 2D images through calculating projections vector and translation vector. Experimental results show that our method can model 3D human face with high accuracy and efficiency.

3-D FACE MODELING AND ITS APPLICATIONS

1996 ◽  
Vol 10 (05) ◽  
pp. 491-520 ◽  
Author(s):  
THOMAS S. HUANG ◽  
LI-AN TANG
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Computer Animation ◽  
Video Sequence ◽  
Human Face ◽  
Face Modeling ◽  
Human Face Recognition ◽  
Preliminary Results ◽  
Human Faces ◽  
Sequence Compression

This paper describes some issues in building a 3-D human face modeling system which mainly consists of three parts: • Modeling human faces; • Analyzing facial motions; • Synthesizing facial expressions. A variety of techniques developed for this system are described in detail in this paper. Some preliminary results of applying this system to computer animation, video sequence compression and human face recognition are also shown.

scholarly journals An Investigation of the Use of Eigen Values in Human Face Modeling for Recognition Tasks

2019 ◽  
pp. 1-9
Author(s):  
Emad F. Khalaf
Keyword(s):  
Face Recognition ◽  
Probabilistic Neural Network ◽  
Moving Average ◽  
Experimental Methodology ◽  
Image Modeling ◽  
Human Face ◽  
Face Modeling ◽  
Moving Average Filter ◽  
The Face ◽  
Average Filter

The face image modeling by eigenvalues is not a new track in the literature. However, a much complete study is required to achieve a comprehensive investigation of the topic. In this research paper, an experimental methodology is conducted for studying the different alternatives of utilizing the eigenvalues for human face recognition. For a better universal investigation, three popular databases are tested; Orl_faces, extended Yale face_A, and extended Yale face_B datasets. The main objective of the study is to find the best choice of using eigenvalues (EV) in face recognition. The technique of the moving average filter (MAF) is combined with that of eigenvalues to enhance the results. Probabilistic neural network (PNN) is used for classification. Three methods of this concept were developed as follows: EV, EV with MAF, and MAF alone. The elapsed time was tested, where for moving average filter was distinctly smaller than the other two methods. For the Yaleface_B database, the eigenvalues method was superior for each of the three training/testing systems. The results were enhanced after using different filters instead of a direct moving average filter to make the proposed method the superior again. The study proved the possibility of using eigenvalues in conjunction with a suitable filter to get acceptable results for all types of image limitations. The concluded ideas elicited from the study spot the light on the usefulness of utilization of eigenvalues in the face recognition tasks.

3D parametric human face modeling for personalized product design: Eyeglasses frame design case

2017 ◽  
Vol 32 ◽  
pp. 202-223 ◽  
Author(s):  
Chih-Hsing Chu ◽  
I-Jan Wang ◽  
Jeng-Bang Wang ◽  
Yuan-Ping Luh
Keyword(s):  
Product Design ◽  
Human Face ◽  
Face Modeling ◽  
Frame Design ◽  
Design Case
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