Wavelet Network and Geometric Features Fusion Using Belief Functions for 3D Face Recognition

2013 ◽  
pp. 307-314 ◽  
Author(s):  
Mohamed Anouar Borgi ◽  
Maher El’Arbi ◽  
Chokri Ben Amar
Keyword(s):  
Face Recognition ◽  
Geometric Features ◽  
3D Face Recognition ◽  
Belief Functions ◽  
Wavelet Network ◽  
3D Face ◽  
Features Fusion

3D fast wavelet network model-assisted 3D face recognition

2015 ◽  
Author(s):  
Salwa Said ◽  
Olfa Jemai ◽  
Mourad Zaied ◽  
Chokri Ben Amar
Keyword(s):  
Face Recognition ◽  
Network Model ◽  
3D Face Recognition ◽  
Wavelet Network ◽  
3D Face ◽  
Fast Wavelet

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.

scholarly journals 3D Face Recognition Using Anthropometric and Curvelet Features Fusion

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Dan Song ◽  
Jing Luo ◽  
Chunyuan Zi ◽  
Huixin Tian
Keyword(s):  
Face Recognition ◽  
Euclidean Distance ◽  
Multiple Scales ◽  
Curvelet Transform ◽  
Facial Feature ◽  
Recognition Algorithm ◽  
3D Face Recognition ◽  
3D Face ◽  
Features Fusion ◽  
Anthropometric Characteristics

Curvelet transform can describe the signal by multiple scales, and multiple directions. In order to improve the performance of 3D face recognition algorithm, we proposed an Anthropometric and Curvelet features fusion-based algorithm for 3D face recognition (Anthropometric Curvelet Fusion Face Recognition, ACFFR). First, the eyes, nose, and mouth feature regions are extracted by the Anthropometric characteristics and curvature features of the human face. Second, Curvelet energy features of the facial feature regions at different scales and different directions are extracted by Curvelet transform. At last, Euclidean distance is used as the similarity between template and objectives. To verify the performance, the proposed algorithm is compared with Anthroface3D and Curveletface3D on the Texas 3D FR database. The experimental results have shown that the proposed algorithm performs well, with equal error rate of 1.75% and accuracy of 97.0%. The algorithm we proposed in this paper has better robustness to expression and light changes than Anthroface3D and Curveletface3D.

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.

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