Research on 3D face recognition method in cloud environment based on semi supervised clustering algorithm

The recognition process of 3D face in cloud environment is vulnerable to the interference of external environment, resulting in poor recognition accuracy, therefore, a 3D face recognition method in cloud environment based on semi supervised clustering algorithm is proposed in this paper, after Harri...

Full description

Saved in:
Bibliographic Details
Published in:Multimedia tools and applications Vol. 76; no. 16; pp. 17055 - 17073
Main Authors: Li, Cuixia, Tan, Yingjun, Wang, Dingbiao, Ma, Peijie
Format: Journal Article
Language:English
Published: New York Springer US 01.08.2017
Springer Nature B.V
Subjects:
Algorithms
Cloud computing
Clustering
Computer Communication Networks
Computer Science
Curvature
Data Structures and Information Theory
Eigenvectors
Face recognition
Facial recognition technology
Feature extraction
Iterative methods
Multimedia Information Systems
Special Purpose and Application-Based Systems
Semi supervised clustering
3D
Cloud environment
Face
Recognition
ISSN:1380-7501, 1573-7721
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The recognition process of 3D face in cloud environment is vulnerable to the interference of external environment, resulting in poor recognition accuracy, therefore, a 3D face recognition method in cloud environment based on semi supervised clustering algorithm is proposed in this paper, after Harris feature points of successful matching of a 2D image are mapped into 3D space, surface fitting method estimated by the least squares is used to extract the curvature information corresponding to feature points, maximum and minimum principal curvatures are constructed as the final curvature eigenvector. The semi supervised clustering algorithm is introduced to perform the cluster judgement to decide if the sample is labeled 3D face sample. According to the class probability or membership degree of 3D face samples to preselect partial samples, labeled 3D face samples training classifier is established, after the second-selection is processed for the preselected 3D face samples according to the classification confidence, the selected results and the predicted labels are added into labeled samples. The iterations are applied to the semi supervised clustering algorithm to cluster the original labeled samples, new labeled samples and label the rest of the unlabeled samples,. The iterative process is repeated until all samples have been marked, and the labeled samples are identified 3D face data. The experiment combined proposed method with cloud computing platform, the experimental results show that the proposed method has high recognition accuracy and efficiency.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-016-3670-1