InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wild

• URL: http://arxiv.org/abs/2110.08935v1
• Date: Sun, 17 Oct 2021 22:12:16 GMT
• Title: InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wild
• Authors: M. Wan, S. Zhu, P. Gulati, L. Luan, X. Huang, R. Schwartz-Mette, M. Hayes, E. Zimmerman, and S. Ostadabbas
• Abstract summary: InfAnFace is a richly-annotated dataset of infant faces. We benchmark the performance of existing facial landmark estimation algorithms trained on adult faces. We put forward the next potential steps to bridge that gap.
• Score: 0.2786153781225931
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: There is promising potential in the application of algorithmic facial landmark estimation to the early prediction, in infants, of pediatric developmental disorders and other conditions. However, the performance of these deep learning algorithms is severely hampered by the scarcity of infant data. To spur the development of facial landmarking systems for infants, we introduce InfAnFace, a diverse, richly-annotated dataset of infant faces. We use InfAnFace to benchmark the performance of existing facial landmark estimation algorithms that are trained on adult faces and demonstrate there is a significant domain gap between the representations learned by these algorithms when applied on infant vs. adult faces. Finally, we put forward the next potential steps to bridge that gap.

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