scholarly journals Biological landmark Vs quasi-landmarks for 3D face recognition and gender classification

2019 ◽  
Vol 9 (5) ◽  
pp. 4069
Author(s):  
Hawraa H. Abbas ◽  
Ammar A. Altameemi ◽  
Hameed R. Farhan
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Face Recognition ◽  
Experimental Work ◽  
Computer Graphic ◽  
Gender Classification ◽  
3D Face Recognition ◽  
3D Face ◽  
Novel Approach ◽  
And Gender

Face recognition and gender classification are vital topics in the field of computer graphic and pattern recognition. We utilized ideas from two growing ideas in computer vision, which are biological landmarks and quasi-landmarks (dense mesh) to propose a novel approach to compare their performance in face recognition and gender classification. The experimental work is conducted on FRRGv2 dataset and acquired 98% and 94% face recognition accuracies using the quasi and biological landmarks respectively. The gender classification accuracies are 92% for quasi-landmarks and 90% for biological landmarks.

scholarly journals 3D Face Recognition based on Local Conformal Parameterization and Iso-Geodesic Stripes Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Chenlei Lv ◽  
Junli Zhao
Keyword(s):  
Face Recognition ◽  
Computer Graphic ◽  
Geometric Features ◽  
3D Face Recognition ◽  
3D Face ◽  
Parameter Domain ◽  
Face Matching ◽  
Novel Approach ◽  
High Recognition Accuracy ◽  
Position Measure

3D face recognition is an important topic in the field of pattern recognition and computer graphic. We propose a novel approach for 3D face recognition using local conformal parameterization and iso-geodesic stripes. In our framework, the 3D facial surface is considered as a Riemannian 2-manifold. The surface is mapped into the 2D circle parameter domain using local conformal parameterization. In the parameter domain, the geometric features are extracted from the iso-geodesic stripes. Combining the relative position measure, Chain 2D Weighted Walkthroughs (C2DWW), the 3D face matching results can be obtained. The geometric features from iso-geodesic stripes in parameter domain are robust in terms of head poses, facial expressions, and some occlusions. In the experiments, our method achieves a high recognition accuracy of 3D facial data from the Texas3D and Bosphorus3D face database.

A Rapid Method for 3D Face Recognition Based on Rejection Algorithm

2010 ◽  
Vol 36 (1) ◽  
pp. 153-158 ◽  
Author(s):  
Xiao-Li LI ◽  
Fei-Peng DA
Keyword(s):  
Face Recognition ◽  
Rapid Method ◽  
3D Face Recognition ◽  
3D Face ◽  
Rejection Algorithm

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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