NDLI: Real-world gender classification via local Gabor binary pattern and three-dimensional face reconstruction by generic elastic model

Content Provider	IET Digital Library
Author	Moeini, Ali Faez, Karim Moeini, Hossein
Abstract	In this study, a novel method is proposed for gender classification by adding facial depth features to texture features. Accordingly, the three-dimensional (3D) generic elastic model is used to reconstruct the 3D model from human face using only a single 2D frontal image. Then, the texture and depth are extracted from the reconstructed face model. Afterwards, the local Gabor binary pattern (LGBP) is applied to both facial texture and reconstructed depth to extract the feature vectors from both texture and reconstructed depth images. Finally, by combining 2D and 3D feature vectors, the final LGBP histogram bins are generated and classified by the support vector machine. Favourable outcomes are acquired for gender classification on the labelled faces in the wild and FERET databases based on the proposed method compared to several state-of-the-arts in gender classification.
Starting Page	690
Ending Page	698
Page Count	9
ISSN	17519659
Volume Number	9
e-ISSN	17519667
Issue Number	Issue 8, Aug (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/9/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2014.0733
Journal	IET Image Processing
Publisher Date	2015-08-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	3D Face Reconstruction 3D Generic Elastic Model 3D Model Reconstruction Computer Vision And Image Processing Technique Face Recognition Facial Depth Feature Extraction Feature Extraction Feature Vector Extraction FERET Database Gender Issues Graphics Technique Image Classification Image Recognition Image Reconstruction Image Texture Knowledge Engineering Technique LGBP Histogram Bins Local Gabor Binary Pattern Real-world Gender Classification Single 2D Frontal Image Solid Modelling Support Vector Machine Texture Feature Extraction
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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