Random-Profiles-Based 3D Face Recognition System
| Autor: | Sunjin Yu, Sangyoun Lee, Joongrock Kim |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Computer science
Point cloud lcsh:Chemical technology Online Systems Biochemistry Facial recognition system Article Pattern Recognition Automated Analytical Chemistry Image Processing Computer-Assisted Humans Three-dimensional face recognition lcsh:TP1-1185 Computer vision Electrical and Electronic Engineering Face detection Instrumentation 3D face recognition business.industry three-dimensional (3D) face modeling Models Theoretical Face Recognition Grand Challenge Atomic and Molecular Physics and Optics Stereopsis Face Face (geometry) Line (geometry) Artificial intelligence business Algorithms face recognition |
| Zdroj: | Sensors, Vol 14, Iss 4, Pp 6279-6301 (2014) Sensors (Basel, Switzerland) Sensors Volume 14 Issue 4 Pages 6279-6301 |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s140406279 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|