Geometrical and Visual Feature Quantization for 3D Face Recognition

被引：0
|
作者：
Hariri, Walid [1 ,2 ]
Tabia, Hedi [1 ]
Farah, Nadir [2 ]
Declercq, David [1 ]
Benouareth, Abdallah [2 ]
机构：
[1] Univ Cergy Pontoise, CNRS, ETIS, ENSEA,UMR 8051, Cergy Pontoise, France
[2] Badji Mokhtar Annaba Univ, Comp Sci Dept, Labged Lab, Annaba, Algeria
来源：
PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 5 | 2017年
关键词：
LBP; HoS; Bag-of-Features; Codebook; Depth Image; Term Vector;
D O I：
10.5220/0006101701870193
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we present an efficient method for 3D face recognition based on vector quantization of both geometrical and visual proprieties of the face. The method starts by describing each 3D face using a set of orderless features, and use then the Bag-of-Features paradigm to construct the face signature. We analyze the performance of three well-known classifiers: the Naive Bayes, the Multilayer perceptron and the Random forests. The results reported on the FRGCv2 dataset show the effectiveness of our approach and prove that the method is robust to facial expression.
引用
收藏
页码：187 / 193
页数：7
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