The influence of ethnicity in facial gender estimation
E. K. Loo, Tien Sze Lim, Lee-Yeng Ong, Chaehwan Lim · 2018
Facial gender estimation is very famous nowadays. It has been widely applied in many applications such as biometrics, targeted advertising and human-computer interaction. Malaysia is a developed country that begins to utilize the mentioned technology. Until today, there are plenty of algorithms for facial gender estimation have been developed. For our best knowledge, no researcher ever publish a benchmark of existing algorithms with Malaysian ethnics' database. Moreover, there is no publicly available Malaysia ethnics' database. Hence, the performance of the existing algorithms towards Malaysian is unknown. Other than that, different ethnics have differences in physical look. Malaysia is a multiracial country that consists of multiple races. Thus, Malaysian ethnics may look different with other ethnics such as westerners for computer point of view. The paper provides an experimental study to shows the gender estimation performance of the existing algorithms towards Malaysian ethnics. Most importantly is to find out the influence of ethnicity in gender estimation. To find out the influences of ethnics, Facial Recognition Technology (FERET) Database and Malaysian Ethnics Facial Database (MEFD) are used to represent non-Malaysian ethnics and Malaysian ethnics. Two feature extraction methods namely Principle Component Analysis (PCA) and Multi-Level Local Binary Patterns (ML-LBP) are chosen to conduct the experiments independently with Support Vector Machine (SVM) as a classifier. All the experiments are conducted without face alignment. The result shows that ML-LBP achieved better accuracy than PCA in gender estimation for both Malaysian ethnics and non-Malaysian ethnics. Last but not least, the ethnicity does affect the gender estimation. The training set has to involve the target's ethnicity in order to have a good accuracy.