Abstract
In this paper, the task of gender and age recognition is performed on pedestrian still images, which are usually captured in-the-wild with no near face-frontal information. Moreover, another difficulty originates from the underlying class imbalance in real examples, especially for the age estimation problem. The scope of the paper is to examine how different loss functions in convolutional neural networks (CNN) perform under the class imbalance problem. For this purpose, as a backbone, we employ the Residual Network (ResNet). On top of that, we attempt to benefit from appearance-based attributes, which are inherently present in the available data. We incorporate this knowledge in an autoencoder, which we attach to our baseline CNN for the combined model to jointly learn the features and increase the classification accuracy. Finally, all of our experiments are evaluated on two publicly available datasets.
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Acknowledgements
This work has been co-funded by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE (project code: T1EDK-04517). The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. All statements of fact, opinion or conclusions contained herein are those of the authors and should not be construed as representing the official views or policies of the sponsors.
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Chatzitzisi, G., Vrigkas, M., Nikou, C. (2020). Gender and Age Estimation Without Facial Information from Still Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12509. Springer, Cham. https://doi.org/10.1007/978-3-030-64556-4_38
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DOI: https://doi.org/10.1007/978-3-030-64556-4_38
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