Gender Classification Based on Multiscale Facial Fusion Feature
Joint Authors
Shang, Yuanyuan
Zhang, Chunyu
Ding, Hui
Shao, Zhuhong
Fu, Xiaoyan
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-04
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
For gender classification, we present a new approach based on Multiscale facial fusion feature (MS3F) to classify gender from face images.
Fusion feature is extracted by the combination of Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) descriptors, and a multiscale feature is generated through Multiblock (MB) and Multilevel (ML) methods.
Support Vector Machine (SVM) is employed as the classifier to conduct gender classification.
All the experiments are performed based on the Images of Groups (IoG) dataset.
The results demonstrate that the application of Multiscale fusion feature greatly improves the performance of gender classification, and our approach outperforms the state-of-the-art techniques.
American Psychological Association (APA)
Zhang, Chunyu& Ding, Hui& Shang, Yuanyuan& Shao, Zhuhong& Fu, Xiaoyan. 2018. Gender Classification Based on Multiscale Facial Fusion Feature. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-6.
https://search.emarefa.net/detail/BIM-1205938
Modern Language Association (MLA)
Zhang, Chunyu…[et al.]. Gender Classification Based on Multiscale Facial Fusion Feature. Mathematical Problems in Engineering No. 2018 (2018), pp.1-6.
https://search.emarefa.net/detail/BIM-1205938
American Medical Association (AMA)
Zhang, Chunyu& Ding, Hui& Shang, Yuanyuan& Shao, Zhuhong& Fu, Xiaoyan. Gender Classification Based on Multiscale Facial Fusion Feature. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-6.
https://search.emarefa.net/detail/BIM-1205938
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1205938