Neuro-fuzzy model for estimating race and gender from geometric distances of human face across pose
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 estimat...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://eprints.unisza.edu.my/1715/1/FH03-FIK-18-13685.jpg http://eprints.unisza.edu.my/1715/2/FH03-FIK-19-23938.pdf http://eprints.unisza.edu.my/1715/ |
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| Summary: | 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. |
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