3D Face Recognition Based on Regional Shape Maps

2018 ◽  
Vol 22 (1) ◽  
pp. 141-146 ◽  
Author(s):  
Xiaoni Wang ◽  
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
3D Face Recognition ◽  
Face Database ◽  
Registration Method ◽  
3D Face ◽  
Feature Vectors ◽  
Input Face ◽  
Point To Point ◽  
3D Face Database

This study proposes an iterative closest shape point (ICSP) registration method based on regional shape maps for 3D face recognition. A neutral expression image randomly selected from a face database is considered as the reference face. The point-to-point correspondences between the input face and the reference face are achieved by constructing the points’ regional shape maps. The distance between corresponding point pairs is then minimized by iterating through the correspondence findings and coordinate transformations. The vectors composed of the closest shape points obtained in the last iteration are regarded as the feature vectors of the input face. These 3D face feature vectors are finally used for both training and recognition using the Fisherface method. Experiments are conducted using the 3D face database maintained by the Chinese Academy of Science Institute of Automation (CASIA). The results show that the proposed method can effectively improve 3D face recognition performance.

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.

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.

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.

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.

3D face recognition based on matching of facial surfaces

2015 ◽  
Author(s):  
Beatriz A. Echeagaray-Patrón ◽  
Vitaly Kober
Keyword(s):  
Face Recognition ◽  
3D Face Recognition ◽  
3D Face
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