X. Wu, X. Feng, E. Boutellaa and A. Hadid, "Kinship Verification using Color Features and Extreme Learning Machine," 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP), Shenzhen, 2018, pp. 187-191. doi: 10.1109/SIPROCESS.2018.8600423
Kinship verification using color features and extreme learning machine
|Author:||Wu, Xiaoting1,2; Feng, Xiaoyi2; Boutellaa, Elhocine1;|
1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
2School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201902266261
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-02-26
Kinship verification from faces is a challenging task that is attracting an increasing attention in the recent years. The proposed methods so far are not robust enough to predict the kin between persons via facial appearance only. The initial studies using deep convolutional neural networks (CNN) have not shown their full potential as well, mainly due to limited training data. To mitigate this problem, we propose a new approach to kinship verification based on color features and extreme learning machines (ELM). While ELM aims to deal with small size training sets, color features are proven to provide significant enhancement over gray-scale counterparts. We evaluate our proposed method on three benchmark and publicly available kinship databases, namely KinFaceW-I, KinFaceW-II and TSKinFace. The obtained results compares favorably against some state-of-the-art methods including those based on deep learning.
|Pages:||187 - 191|
2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP)
International Conference on Signal and Image Processing
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
113 Computer and information sciences
This work was partially funded by China Scholarship Council and Academy of Finland.
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