LEARNING EFFICIENT CODES FOR 3D FACE RECOGNITION

被引：10

作者：

Zhong, Cheng ^{[1
]}

Sun, Zhenan ^{[1
]}

Tan, Tieniu ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China

来源：

2008 15TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-5 | 2008年

关键词：

Face recognition; Pattern clustering methods; Image texture analysis; Pattern recognition; Image analysis;

D O I：

10.1109/ICIP.2008.4712158

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Face representation based on the Visual Codebook becomes popular because of its excellent recognition performance, in which the critical problem is how to learn the most efficient codes to represent the facial characteristics. In this paper, we introduce the Quadtree clustering algorithm to learn the facial-codes to boost 3D face recognition performance. The merits of Quadtree clustering come from: (1) It is robust to data noises; (2) It can adaptively assign clustering centers according to the density of data distribution. We make a comparison between Quadtree and some widely used clustering methods, such as G-means, K-means, Normalized-cut and Mean-shift. Experimental results show that using the facial-codes learned by Quadtree clustering gives the best performance for 3D face recognition.

引用

页码：1928 / 1931

页数：4

共 50 条

[1] Efficient 3D reconstruction for face recognition
Jiang, DL
Hu, YX
Yan, SC
Zhang, L
Zhang, HJ
Gao, W
[J]. PATTERN RECOGNITION, 2005, 38 (06) : 787 - 798
[2] Efficient 3D Face Recognition in Uncontrolled Environment
Ding, Yuqi
Li, Nianyi
Young, S. Susan
Ye, Jinwei
[J]. ADVANCES IN VISUAL COMPUTING, ISVC 2019, PT I, 2020, 11844 : 430 - 443
[3] A Heuristic Approach to 3D Face Modelling for Efficient Face Recognition
Chakraborti, Tathagata
Sengupta, Abhronil
Konar, Amit
Ramadoss, Janarthanan
[J]. 2012 12TH INTERNATIONAL CONFERENCE ON HYBRID INTELLIGENT SYSTEMS (HIS), 2012, : 312 - 317
[4] 3D Face Recognition Based on Deep Learning
Luo, Jing
Hu, Fei
Wang, Ruihuan
[J]. 2019 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (ICMA), 2019, : 1576 - 1581
[5] LEARNING EFFECTIVE FEATURES FOR 3D FACE RECOGNITION
Ming, Yue
Ruan, Qiuqi
Ni, Rongrong
[J]. 2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, 2010, : 2421 - 2424
[6] 3D face recognition
Beumier, C
[J]. CIHSPS 2004: PROCEEDINGS OF THE 2004 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR HOMELAND SECURITY AND PERSONAL SAFETY, 2004, : 93 - 96
[7] 3D face recognition
Beumier, Charles
[J]. 2006 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY, VOLS 1-6, 2006, : 2896 - 2901
[8] 3D face recognition
Dutagaci, Helin
Sankur, Bulent
Yemez, Yucel
[J]. 2006 IEEE 14TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS, VOLS 1 AND 2, 2006, : 786 - +
[9] Robust Learning from Normals for 3D Face Recognition
Marras, Ioannis
Zafeiriou, Stefanos
Tzimiropoulos, Georgios
[J]. COMPUTER VISION - ECCV 2012, PT II, 2012, 7584 : 230 - 239
[10] Learning 3D Face Representation with Vision Transformer for Masked Face Recognition
Wang, Yuan
Yang, Zhen
Zhang, Zhiqiang
Zang, Huaijuan
Zhu, Qiang
Zhan, Shu
[J]. 2022 ASIA CONFERENCE ON ALGORITHMS, COMPUTING AND MACHINE LEARNING (CACML 2022), 2022, : 505 - 511

← 1 2 3 4 5 →