Abstract
Traditional face recognition models based on deep learning are often affected by the subjective ideas and prior knowledge of developers in terms of feature extraction and model construction. They cannot achieve the most effective results and consume a lot of human resources. In this paper, we proposed a way to build a face recognition model based on Automatic Machine Learning. We performed manual data augmentation on the original dataset called FaceScrub, used the EasyDL automatic machine learning platform, and found the relatively optimal model by adjusting the hyperparameters of the platform. Experiments showed that the effect of data training after manual augmentation is significantly better than that of platform automatic data augmentation. The top-1 recognition accuracy of the final model reached 99%, and the F1-score, Precision Rate and Recall Rate all reached 99%.
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Shi, Y., Tang, H., Wang, Z., Zhu, W. (2023). Automatic Machine Learning Model Construction and Performance Validation in Face Recognition. In: Kountchev, R., Nakamatsu, K., Wang, W., Kountcheva, R. (eds) Proceedings of the World Conference on Intelligent and 3-D Technologies (WCI3DT 2022). Smart Innovation, Systems and Technologies, vol 323. Springer, Singapore. https://doi.org/10.1007/978-981-19-7184-6_3
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DOI: https://doi.org/10.1007/978-981-19-7184-6_3
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