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

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... e 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. | INTRODUCTION In recent years, facial analysis tasks have become a very popular research topic. Among these tasks, age and gender classification provides basic and essential information about the human face. The information obtained from age and gender classification can be valuable to a wide variety of applications, such as automatic surveillance [1], targeted advertising [2] and so on. In some previous studies, [3] [4] [5] [6] showed promising results in constrained environment settings. Even with the promising results in [3-6], there is still room for improvements in real-life and unconstrained situations. There are challenges with reallife and unconstrained situations, such as the subject's face being obstructed, images with low resolution, blurry images, extreme lighting conditions or subjects with missing distinctive features, that even humans cannot identify easily. Deep learning methods have proved a lot of success compared to the traditional machine learning methods in age and gender classification tasks. The most recent state-of-theart methods [8] [9] [10] [11] all use convolutional neural networks (CNN). When training a CNN, the training data plays a This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.