Abstract
Traditionally, researchers train facial gender and age recognition models separately using deep convolutional networks. However, in the real world, it is crucial to build a low-cost and time-efficient multitask learning system that can simultaneously recognize both these tasks. In multitask learning, the synergy among the tasks creates imbalance in the loss functions and influences their individual performances. This imbalance among the task-specific loss functions leads to a drop in accuracy. To overcome this challenge and achieve better performance, we propose a novel weighted sum of loss functions that balances the loss of each task. We train our method for the recognition of gender and age on the publicly available Adience benchmark dataset. Finally, we experiment our method on VGGFace and FaceNet architectures and evaluate on the Adience test set to achieve better performance than previous architectures.
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Acknowledgements
We thank Korea Research Institute of Ships and Ocean Engineering as well as Korea Advanced Institute of Science and Technology for the infrastructure and resources provided to us to complete this paper.
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Nanda, A., Yang, HS. (2024). Multitask Learning-Based Simultaneous Facial Gender and Age Recognition with a Weighted Loss Function. In: Borah, M.D., Laiphrakpam, D.S., Auluck, N., Balas, V.E. (eds) Big Data, Machine Learning, and Applications. BigDML 2021. Lecture Notes in Electrical Engineering, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-99-3481-2_7
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DOI: https://doi.org/10.1007/978-981-99-3481-2_7
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