Abstract
Automatic facial gender prediction is an essential sub-task of any facial analysis system. Given the current rapid advancements of artificial intelligence and the ease of connectivity, gender prediction models are being utilised in many applications, such as soft biometrics, surveillance, targeted marketing, social media, human-computer interaction applications and many more. Nonetheless, when gender prediction models attempt to classify images taken in unrestrained conditions with different poses and illuminations, the classification accuracy drops significantly. One solution to solve this issue is employing a very deep architecture to extract as many features as possible from an image. However, such models are usually complex to train and might not be deployable to devices with limited computation power. Therefore, the main objective of this study is to close the gap between the classification of unrestrained facial images and the need for an efficient architecture. We achieve this objective by presenting a novel lightweight deep convolutional neural network to classify male and female facial images taken in uncontrolled environments. We train our model on the UTKFace dataset and produce competitive accuracies compared to several state-of-the-art methods. We then carefully analyse and discuss our results to better understand the variables that control the performance of our model. In addition, we present potential suggestions and ideas to enhance our proposed method for future research.
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ELKarazle, K., Raman, V., Then, P. (2022). Facial Gender Classification Using Deep Learning. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_56
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DOI: https://doi.org/10.1007/978-3-030-96302-6_56
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