Nanaa, K and Rahman, M.N.A. and Rizon, M. and Mohamad, F.S. and Mamat, M. (2018) Neuro-fuzzy model for estimating race and gender from geometric distances of human face across pose. In: : International Conference on Operations Research of the Indonesian-Operations-Research-Association (IORA), 12 Oct 2017, Tangerang Selatan, Indonesia.
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Abstract
Classifying human face based on race and gender is a vital process in face recognition. It contributes to an index database and eases 3D synthesis of the human face. Identifying race and gender based on intrinsic factor is problematic, which is more fitting to utilizing nonlinear model for estimating process. In this paper, we aim to estimate race and gender in varied head pose. For this purpose, we collect dataset from PICS and CAS-PEAL databases, detect the landmarks and rotate them to the frontal pose. Aer geometric distances are calculated, all of distance values will be normalized. Implementation is carried out by using Neural Network Model and Fuzzy Logic Model. These models are combined by using Adaptive Neuro-Fuzzy Model. The experimental results showed that the optimization of address fuzzy membership. Model gives a better assessment rate and found that estimating race contributing to a more accurate gender assessment.
Item Type: | Conference or Workshop Item (Paper) |
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Uncontrolled Keywords: | classification |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
Divisions: | Faculty of Informatics & Computing |
Depositing User: | Muhammad Akmal Azhar |
Date Deposited: | 22 Nov 2020 02:53 |
Last Modified: | 22 Nov 2020 02:53 |
URI: | http://eprints.unisza.edu.my/id/eprint/1715 |
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