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Age and gender estimation using Region-SIFT and multi-layered SVM

Title
Age and gender estimation using Region-SIFT and multi-layered SVM
Authors
Kim, HyundukLee, Sang HeonSohn, Myoung KyuHwang, Byung Hun
DGIST Authors
Lee, Sang HeonSohn, Myoung Kyu
Issue Date
2017-11-14
Citation
ICMV 2017
Type
Conference
ISBN
9781510619418
ISSN
0277-786X
Abstract
In this paper, we propose an age and gender estimation framework using the region-SIFT feature and multi-layered SVM classifier. The suggested framework entails three processes. The first step is landmark based face alignment. The second step is the feature extraction step. In this step, we introduce the region-SIFT feature extraction method based on facial landmarks. First, we define sub-regions of the face. We then extract SIFT features from each sub-region. In order to reduce the dimensions of features we employ a Principal Component Analysis (PCA) and a Linear Discriminant Analysis (LDA). Finally, we classify age and gender using a multi-layered Support Vector Machines (SVM) for efficient classification. Rather than performing gender estimation and age estimation independently, the use of the multi-layered SVM can improve the classification rate by constructing a classifier that estimate the age according to gender. Moreover, we collect a dataset of face images, called by DGIST-C, from the internet. A performance evaluation of proposed method was performed with the FERET database, CACD database, and DGIST-C database. The experimental results demonstrate that the proposed approach classifies age and performs gender estimation very efficiently and accurately. © 2018 Copyright SPIE.
URI
http://hdl.handle.net/20.500.11750/6361
DOI
10.1117/12.2309441
Publisher
SPIE
Related Researcher
Files:
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Collection:
Convergence Research Center for Future Automotive Technology2. Conference Papers


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