scholarly journals A New Face Recognition Method for Intelligent Security

2020 ◽  
Vol 10 (3) ◽  
pp. 852 ◽  
Author(s):  
Zhenzhou Wang ◽  
Xu Zhang ◽  
Pingping Yu ◽  
Wenjie Duan ◽  
Dongjie Zhu ◽  
...  
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Recognition Method ◽  
3D Face Reconstruction ◽  
Recognition Time ◽  
Security Industry ◽  
Face Reconstruction ◽  
Weighted Fusion ◽  
Intelligent Security

With the advent of the era of artificial intelligence and big data, intelligent security robots not only improve the efficiency of the traditional intelligent security industry but also propose higher requirements for intelligent security. Aiming to solve the problem of long recognition time and high equipment cost of intelligent security robots, we propose a new face recognition method for intelligent security in this paper. We use the Goldstein branching method for phase unwrapping, which can improve the three-dimensional (3D) face reconstruction effect. Subsequently, by using the three-dimensional face recognition method based on face radial curve elastic matching, different weights are assigned to different curve recognition similarity for weighted fusion as the total similarity for recognition. Experiments show that the method has a higher face recognition rate and is robust to attitude, illumination, and noise.

scholarly journals Research on an Improved MB-LBP 3D Face Recognition Method

2021 ◽  
pp. 306-314
Author(s):  
Liangliang Shi ◽  
◽  
Xia Wang ◽  
Yongliang Shen
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Depth Image ◽  
Support Vector ◽  
3D Face Recognition ◽  
Recognition Method ◽  
Recognition Time ◽  
3D Face ◽  
Feature Information ◽  
Average Information

In order to improve the accuracy and speed of 3D face recognition, this paper proposes an improved MB-LBP 3D face recognition method. First, the MB-LBP algorithm is used to extract the features of 3D face depth image, then the average information entropy algorithm is used to extract the effective feature information of the image, and finallythe Support Vector Machine algorithm is used to identify the extracted effective information. The recognition rate on the Texas 3DFRD database is 96.88%, and the recognition time is 0.025s. The recognition rate in the self-made depth library is 96.36%, and the recognition time is 0.02s.It can be seen from the experimental results that the algorithm in this paper has better performance in terms of accuracy and speed.

Machine-Printed Invoice Number Based on Fuzzy Recognition

2012 ◽  
Vol 214 ◽  
pp. 705-710 ◽  
Author(s):  
Xiao Ping Xian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Real System ◽  
Recognition Method ◽  
Recognition Time ◽  
Recognition Ability ◽  
System Requirements ◽  
Fuzzy Recognition ◽  
Detection And Separation

A new fuzzy recognition method of machine-printed invoice number based on neural network is presented. This method includes ten links: invoice number detection and separation of right on top of invoice, binarization, denoising, incline correction, extraction of invoice code numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. Through testing, the recognition rate of this method can be over 99%.The recognition time of characters for character is less than 1 second, which means that the method is of more effective recognition ability and can better satisfy the real system requirements.

3D Face Reconstruction from Two Orthogonal Images for Face Recognition Applications

2012 ◽  
pp. 223-239
Author(s):  
Stefano Berretti ◽  
Alberto Del Bimbo ◽  
Pietro Pala
Keyword(s):  
Face Recognition ◽  
Control Points ◽  
3D Face Reconstruction ◽  
3D Face ◽  
Face Reconstruction ◽  
Face Images ◽  
Model Control ◽  
3D Geometry ◽  
The Face ◽  
Manual Assistance

In this paper, an original hybrid 2D-3D face recognition approach is proposed using two orthogonal face images, frontal and side views of the face, to reconstruct the complete 3D geometry of the face. This is obtained using a model based solution, in which a 3D template face model is morphed according to the correspondence of a limited set of control points identified on the frontal and side images in addition to the model. Control points identification is driven by an Active Shape Model applied to the frontal image, whereas subsequent manual assistance is required for control points localization on the side view. The reconstructed 3D model is finally matched, using the iso-geodesic regions approach against a gallery of 3D face scans for the purpose of face recognition. Preliminary experimental results are provided on a small database showing the viability of the approach.

Single View 3D Face Reconstruction

2020 ◽  
pp. 215-227
Author(s):  
Claudio Ferrari ◽  
Stefano Berretti ◽  
Alberto del Bimbo
Keyword(s):  
3D Structure ◽  
Three Dimensional ◽  
3D Face Reconstruction ◽  
3D Face ◽  
Single View ◽  
Face Reconstruction ◽  
Current State ◽  
Rgb Images ◽  
Human Faces ◽  
Rgb Image

3D face reconstruction from a single 2D image is a fundamental computer vision problem of extraordinary difficulty that dates back to the 1980s. Briefly, it is the task of recovering the three-dimensional geometry of a human face from a single RGB image. While the problem of automatically estimating the 3D structure of a generic scene from RGB images can be regarded as a general task, the particular morphology and non-rigid nature of human faces make it a challenging problem for which dedicated approaches are still currently studied. This chapter aims at providing an overview of the problem, its evolutions, the current state of the art, and future trends.

Sign in / Sign up

Export Citation Format

Share Document