scholarly journals Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Naeem Ratyal ◽  
Imtiaz Ahmad Taj ◽  
Muhammad Sajid ◽  
Anzar Mahmood ◽  
Sohail Razzaq ◽  
...  
Keyword(s):  
Face Recognition ◽  
Superior Performance ◽  
Support Vector ◽  
Complex Nature ◽  
Alignment Algorithm ◽  
3D Face Recognition ◽  
3D Face ◽  
Norm Minimization ◽  
Wide Range ◽  
The Face

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.

Novel Technique for 3D Face Recognition Using Anthropometric Methodology

2018 ◽  
Vol 9 (1) ◽  
pp. 60-77 ◽  
Author(s):  
Souhir Sghaier ◽  
Wajdi Farhat ◽  
Chokri Souani
Keyword(s):  
Face Recognition ◽  
Learning Algorithm ◽  
Recognition Performance ◽  
Experimental Tests ◽  
3D Face Recognition ◽  
3D Face ◽  
Area Of Interest ◽  
The Face ◽  
Face Datasets ◽  
Improved Technique

This manuscript presents an improved system research that can detect and recognize the person in 3D space automatically and without the interaction of the people's faces. This system is based not only on a quantum computation and measurements to extract the vector features in the phase of characterization but also on learning algorithm (using SVM) to classify and recognize the person. This research presents an improved technique for automatic 3D face recognition using anthropometric proportions and measurement to detect and extract the area of interest which is unaffected by facial expression. This approach is able to treat incomplete and noisy images and reject the non-facial areas automatically. Moreover, it can deal with the presence of holes in the meshed and textured 3D image. It is also stable against small translation and rotation of the face. All the experimental tests have been done with two 3D face datasets FRAV 3D and GAVAB. Therefore, the test's results of the proposed approach are promising because they showed that it is competitive comparable to similar approaches in terms of accuracy, robustness, and flexibility. It achieves a high recognition performance rate of 95.35% for faces with neutral and non-neutral expressions for the identification and 98.36% for the authentification with GAVAB and 100% with some gallery of FRAV 3D datasets.

scholarly journals 3D Face Recognition Analysis Using Random Forest

2019 ◽  
Vol 2 (2) ◽  
pp. c1-7
Author(s):  
NURUL ATIFAH ROSLAN ◽  
HAMIMAH UJIR ◽  
IRWANDI HIPNI MOHAMAD HIPINY
Keyword(s):  
Face Recognition ◽  
Random Forest ◽  
Recognition Rate ◽  
Support Vector ◽  
3D Face Recognition ◽  
Security Sector ◽  
3D Face ◽  
Random Forest Method ◽  
Technological Advances ◽  
Fear Expression

Face recognition is an emerging field due to the technological advances in camera hardware and for its application in various fields such as the commercial and security sector. Although the existing works in 3D face recognition perform well, a similar experiment setting across classifiers is hard to find, which includes the Random Forest classifier. The aggregations of the classification from each decision tree are the outcome of Random Forest. This paper presents 3D facial recognition using the Random Forest method using the BU-3DFE database, which consists of basic facial expressions. The work using other classifiers such as Neural Network (NN) and Support Vector Machine (SVM) using a similar experiment setting also presented. As for the results, the Random Forest approach has yield 94.71% of recognition rate, which is an encouraging result compared to NN and SVM. In addition, the experiment also yields that fear expression is unique to each human due to a high confidence rate (82%) of subjects with fear expression. Therefore, a lower chance to be mistakenly recognized someone with a fear expression.

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.

3D Face Recognition in the Presence of Partial Data

2014 ◽  
pp. 70-97
Author(s):  
Dat Chu ◽  
Shishir Shah ◽  
Ioannis A. Kakadiaris
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition Performance ◽  
Recognition Task ◽  
Training Data ◽  
3D Face Recognition ◽  
Coupling Term ◽  
3D Face ◽  
The Face ◽  
The Right

Performing face recognition under extreme poses and lighting conditions remains a challenging task for current state-of-the-art biometric algorithms. The recognition task is even more challenging when there is insufficient training data available in the gallery, or when the gallery dataset originates from one side of the face while the probe dataset originates from the other. The authors present a new method for computing the distance between two biometric signatures acquired under such challenging conditions. This method improves upon an existing Semi-Coupled Dictionary Learning method by computing a jointly-optimized solution that incorporates the reconstruction cost, the discrimination cost, and the semi-coupling cost. The use of a semi-coupling term allows the method to handle partial 3D face meshes where, for example, only the left side of the face is available for gallery and the right side of the face is available for probe. The method also extends to 2D signatures under varying poses and lighting changes by using 3D signatures as a coupling term. The experiments show that this method can improve recognition performance of existing state-of-the-art wavelet signatures used in 3D face recognition and provide excellent recognition results in the 3D-2D face recognition application.

3D Face Recognition Using Spatial Relations

2018 ◽  
pp. 679-706
Author(s):  
Stefano Berretti ◽  
Alberto del Bimbo ◽  
Pietro Pala
Keyword(s):  
Face Recognition ◽  
Depth Map ◽  
Spatial Relations ◽  
Future Research ◽  
3D Face Recognition ◽  
Still Images ◽  
3D Face ◽  
Identity Recognition ◽  
3D Data ◽  
The Face

Identity recognition using 3D scans of the face has been recently proposed as an alternative or complementary solution to conventional 2D face recognition approaches based on still images or videos. In fact, face representations based on 3D data are expected to be more robust to pose changes and illumination variations than 2D images, thus allowing accurate face recognition in real-world applications with unconstrained acquisition. Based on these premises, in this chapter, the authors first introduce the general and main methodologies for 3D face recognition, shortly reviewing the related literature by distinguishing between global and local approaches. Then, the authors present and discuss two 3D face recognition approaches that are robust to facial expression variations and share the common idea of accounting for the spatial relations between local facial features. In the first approach, the face is partitioned into iso-geodesic stripes and spatial relations are computed by integral measures that capture the relative displacement between the sets of 3D points in each pair of stripes. In the second solution, the face is described by detecting keypoints in the depth map of the face and locally describing them. Then, facial curves on the surface are considered between each pair of keypoints, so as to capture the shape of the face along the curve as well as the relational information between keypoints. Future research directions and conclusions are drawn at the end of the chapter.

3D Face Recognition using an Adaptive Non-Uniform Face Mesh

2011 ◽  
pp. 562-573
Author(s):  
Wei Jen Chew ◽  
Kah Phooi Seng ◽  
Li-Minn Ang
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Face Image ◽  
Uniform Mesh ◽  
Range Image ◽  
3D Face Recognition ◽  
Linear Discriminant ◽  
3D Face ◽  
The Face

Face recognition using 3D faces has become widely popular in the last few years due to its ability to overcome recognition problems encountered by 2D images. An important aspect to a 3D face recognition system is how to represent the 3D face image. In this chapter, it is proposed that the 3D face image be represented using adaptive non-uniform meshes which conform to the original range image. Basically, the range image is converted to meshes using the plane fitting method. Instead of using a mesh with uniform sized triangles, an adaptive non-uniform mesh was used instead to reduce the amount of points needed to represent the face. This is because some parts of the face have more contours than others, hence requires a finer mesh. The mesh created is then used for face recognition purposes, using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Simulation results show that an adaptive non-uniform mesh is able to produce almost similar recognition rates compared to uniform meshes but with significant reduction in number of vertices.

scholarly journals Geodesic Distance on Riemannian Manifold using Jacobi Iterations in 3D Face Recognition System

2017 ◽  
Vol 6 (1) ◽  
pp. 10
Author(s):  
Rachid Ahdid ◽  
Said Safi ◽  
Mohamed Fakir ◽  
Bouzid Manaut
Keyword(s):  
Face Recognition ◽  
Riemannian Manifold ◽  
Eikonal Equation ◽  
Geodesic Distance ◽  
Recognition System ◽  
Support Vector ◽  
3D Face Recognition ◽  
3D Face ◽  
Face Recognition System ◽  
Jacobi Iterations

In this paper, we present an automatic application of 3D face recognition system using geodesic distance in Riemannian geometry. We consider, in this approach, the three dimensional face images as residing in Riemannian manifold and we compute the geodesic distance using the Jacobi iterations as a solution of the Eikonal equation. The problem of solving the Eikonal equation, unstructured simplified meshes of 3D face surface, such as tetrahedral and triangles are important for accurately modeling material interfaces and curved domains, which are approximations to curved surfaces in R<sup>3</sup>. In the classifying steps, we use: Neural Networks (NN), K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). To test this method and evaluate its performance, a simulation series of experiments were performed on 3D Shape REtrieval Contest 2008 database (SHREC2008).<strong></strong>

3D Face Recognition Using Spatial Relations

2014 ◽  
pp. 98-123
Author(s):  
Stefano Berretti ◽  
Alberto del Bimbo ◽  
Pietro Pala
Keyword(s):  
Face Recognition ◽  
Depth Map ◽  
Spatial Relations ◽  
Future Research ◽  
3D Face Recognition ◽  
Still Images ◽  
3D Face ◽  
Identity Recognition ◽  
3D Data ◽  
The Face

Identity recognition using 3D scans of the face has been recently proposed as an alternative or complementary solution to conventional 2D face recognition approaches based on still images or videos. In fact, face representations based on 3D data are expected to be more robust to pose changes and illumination variations than 2D images, thus allowing accurate face recognition in real-world applications with unconstrained acquisition. Based on these premises, in this chapter, the authors first introduce the general and main methodologies for 3D face recognition, shortly reviewing the related literature by distinguishing between global and local approaches. Then, the authors present and discuss two 3D face recognition approaches that are robust to facial expression variations and share the common idea of accounting for the spatial relations between local facial features. In the first approach, the face is partitioned into iso-geodesic stripes and spatial relations are computed by integral measures that capture the relative displacement between the sets of 3D points in each pair of stripes. In the second solution, the face is described by detecting keypoints in the depth map of the face and locally describing them. Then, facial curves on the surface are considered between each pair of keypoints, so as to capture the shape of the face along the curve as well as the relational information between keypoints. Future research directions and conclusions are drawn at the end of the chapter.

scholarly journals 3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms

2019 ◽  
Vol 19 (2) ◽  
pp. 28-37
Author(s):  
Hawraa H. Abbas ◽  
Bilal Z. Ahmed ◽  
Ahmed Kamil Abbas
Keyword(s):  
Face Recognition ◽  
High Performance ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
3D Face Recognition ◽  
Shape Variation ◽  
3D Face ◽  
The Face

Abstract The face is the preferable biometrics for person recognition or identification applications because person identifying by face is a human connate habit. In contrast to 2D face recognition, 3D face recognition is practically robust to illumination variance, facial cosmetics, and face pose changes. Traditional 3D face recognition methods describe shape variation across the whole face using holistic features. In spite of that, taking into account facial regions, which are unchanged within expressions, can acquire high performance 3D face recognition system. In this research, the recognition analysis is based on defining a set of coherent parts. Those parts can be considered as latent factors in the face shape space. Non-negative matrix Factorisation technique is used to segment the 3D faces to coherent regions. The best recognition performance is achieved when the vertices of 20 face regions are utilised as a feature vector for recognition task. The region-based 3D face recognition approach provides a 96.4% recognition rate in FRGCv2 dataset.

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