Neuro-fuzzy model for estimating race and gender from geometric distances of human face across pose

Indonesian Operations Research Association - International Conference on Operations Research 2017

Nanaa, K.^1 ; Rahman, M.N.A.^1 ; Rizon, M.^2 ; Mohamad, F.S.^1 ; Mamat, M.^1

Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Besut Campus, Besut, Terengganu

22200, Malaysia^1

Faculty of Visual Communication Design, National Academy of Arts Culture and Heritage, Kuala Lumpur

50480, Malaysia^2

关键词: Adaptive neuro-fuzzy;    CAS-PEAL database;    Fuzzy logic model;    Geometric distances;    Intrinsic factors;    Neural network model;    Neuro-Fuzzy model;    Non-linear model;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/332/1/012028/pdf
DOI  :  10.1088/1757-899X/332/1/012028

来源: IOP

【 摘 要 】

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. After 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.