Open AccessData augmentation with occluded facial features for age and gender estimation
Author(s) -
Lin Lu En,
Lin Chang Hong
Publication year - 2021
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/bme2.12030
Subject(s) - computer science , ground truth , artificial intelligence , pattern recognition (psychology) , cross entropy , entropy (arrow of time) , data set , training set , convolution (computer science) , artificial neural network , physics , quantum mechanics
Here, the feature occlusion, a data augmentation method that simulates real‐life challenges on the main features of the human face for age and gender recognition is proposed. Previous methods achieved promising results on constrained data sets with strict environmental settings, but the results on unconstrained data sets are still far from perfect. The proposed method adopted three simple occlusion techniques, blackout, random brightness, and blur, and each simulates a different kind of challenge that would be encountered in real‐world applications. A modified cross‐entropy loss that gives less penalty to the age predictions that land on the adjacent classes of the ground truth class is also proposed. The effectiveness of our proposed method is verified by implementing the augmentation method and modified cross‐entropy loss on two different convolution neural networks, the slightly modified AdienceNet and the slightly modified VGG16, to perform age and gender classification. The proposed augmentation system improves the age and gender classification accuracy of the slightly modified AdienceNet network by 6.62% and 6.53% on the Adience data set, respectively. The proposed augmentation system also improves the age and gender classification accuracy of the slightly modified VGG16 network by 6.20% and 6.31% on the Adience data set, respectively.