Quantifying Facial Gestures Using Deep Learning in a New World Monkey.

ABSTRACT: Facial gestures are a crucial component of primate multimodal communication. However, current methodologies for extracting facial data from video recordings are labor-intensive and prone to human subjectivity. Although automatic tools for this task are still in their infancy, deep learning techniques are revolutionizing animal behavior research. This study explores the distinctiveness of facial gestures in cotton-top tamarins, quantified using markerless pose estimation algorithms. From footage of captive individuals, we extracted and manually labeled frames to develop a model that can recognize a custom set of landmarks positioned on the face of the target species. The trained model predicted landmark positions and subsequently transformed them into distance matrices representing landmark

SUBMITTER: Carugati F 

PROVIDER: S-EPMC11869534 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature