Multi-pose Face Recognition Using Fusion of Scale Invariant Features

被引：0
|
作者：
Wijaya, I. Gede Pasek Suta [1 ,2 ]
Uchimura, Keiichi [2 ]
Koutaki, Gou [2 ]
机构：
[1] Mataram Univ, Dept Elect Engn, J1 Majapahit 62, Mataram, Indonesia
[2] Kumamoto Univ, GSST, Comp Sci & Elect Engn Dept, Kumamoto, Japan
来源：
PROCEEDINGS OF THE 2011 2ND INTERNATIONAL CONGRESS ON COMPUTER APPLICATIONS AND COMPUTATIONAL SCIENCE, VOL 1 | 2012年 / 144卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a new multi-pose face recognition approach using fusion of scale invariant features (FSIF). The FSIF is a face descriptor representing 3D face images features which is created by fusing some scale invariant features extracted by scale invariant features transforms (SIFT) from several different poses of 2D face images. The main aim of this method is to avoid using 3D scanner for estimating any pose variations of a face image but it still have reasonable achievement compare to 3D-based face recognition method for multi-pose face recognition. The experimental results show the proposed method is sufficiently to overcame large face variability due to face pose variations.
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页码：207 / +
页数：3
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