Thumbnail
Access Restriction
Open

Author Kim, Joongrock ♦ Yu, Sunjin ♦ Lee, Sangyoun
Source World Health Organization (WHO)-Global Index Medicus
Content type Text
Publisher Multidisciplinary Digital Publishing Institute
File Format HTM / HTML
Language English
Difficulty Level Medium
Subject Domain (in DDC) Computer science, information & general works ♦ Library & information sciences ♦ Natural sciences & mathematics ♦ Mathematics ♦ Life sciences; biology ♦ Physiology & related subjects ♦ Natural history of organisms ♦ Technology ♦ Medicine & health ♦ Human anatomy, cytology, histology ♦ Human physiology ♦ Diseases ♦ Manufacture for specific uses ♦ Precision instruments & other devices
Subject Domain (in MeSH) Body Regions ♦ Anatomy ♦ Eukaryota ♦ Organisms ♦ Investigative Techniques ♦ Analytical, Diagnostic and Therapeutic Techniques and Equipment ♦ Mathematical Concepts ♦ Biological Sciences ♦ Information Science ♦ Information Science
Subject Keyword Discipline Biotechnology ♦ Face ♦ Anatomy & Histology ♦ Pattern Recognition, Automated ♦ Methods ♦ Algorithms ♦ Humans ♦ Image Processing, Computer-assisted ♦ Models, Theoretical ♦ Online Systems ♦ Journal Article ♦ Research Support, Non-u.s. Gov't
Abstract In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation.
Description Author Affiliation: Kim J ( Department of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-dong, Seodaemun-gu, Seoul 120-749, Korea. jurock@yonsei.ac.kr.); Yu S ( Department of Broadcasting and Film, Cheju Halla University, 38, Halladaehak-ro, Jeju-si, Jeju-do 690-708, Korea. sjyu@chu.ac.kr.); Lee S ( Department of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-dong, Seodaemun-gu, Seoul 120-749, Korea. syleee@yonsei.ac.kr.)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Reading ♦ Research ♦ Self Learning
Interactivity Type Expositive
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-03-31
Publisher Place Switzerland
e-ISSN 14248220
Journal Sensors
Volume Number 14
Issue Number 4


Source: WHO-Global Index Medicus