InfAnFace: Bridging the Infant–Adult Domain Gap in Facial Landmark Estimation in the Wild
InfAnFace: Bridging the Infant–Adult Domain Gap in Facial Landmark Estimation in the Wild
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DOI：
10.1109/icpr56361.2022.9956647
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Michael Wan;S. Zhu;Prateek Gulati;L. Luan;X. Huang;R. Schwartz-Mette;M. Hayes;E. Zimmerman;S. Ostadabbas
中科院分区：
文献类型：
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作者：
Michael Wan;S. Zhu;Prateek Gulati;L. Luan;X. Huang;R. Schwartz-Mette;M. Hayes;E. Zimmerman;S. Ostadabbas
我们为婴儿面部的算法理解研究奠定了基础，预期从医疗保健到心理学的应用，特别是在发育障碍的早期预测方面。具体来说，我们介绍了有史以来第一个用面部地标坐标和姿态属性注释的婴儿面部数据集，展示了婴儿领域现有面部地标估计算法的不足之处，并训练了新的最先进的模型，这些模型使用域自适应技术显着改善了这些算法。我们触及了与婴儿面部检测密切相关的任务，以及我们实验室收集的红外婴儿监视器图像的一个具有挑战性的案例研究，作为对上述发育问题的现场研究的一部分1
We lay the groundwork for research in the algorithmic comprehension of infant faces, in anticipation of applications from healthcare to psychology, especially in the early prediction of developmental disorders. Specifically, we introduce the first-ever dataset of infant faces annotated with facial landmark coordinates and pose attributes, demonstrate the inadequacies of existing facial landmark estimation algorithms in the infant domain, and train new state-of-the-art models that significantly improve upon those algorithms using domain adaptation techniques. We touch on the closely related task of facial detection for infants, and also on a challenging case study of infrared baby monitor images gathered by our lab as part of in-field research into the aforementioned developmental issues1