3D Weighted Geometric Graph Neural Networks for Sheep Facial Pain Assessment

2026-08-11

AI Summary

Traditional 2D approaches struggle to model the three-dimensional anatomical structure of sheep faces and the spatial relationships among facial keypoints, limiting the accuracy of Sheep Pain Facial Expression Scale (SPFES)-based pain assessment. This work proposes 3D-SPFES, a novel system that integrates monocular RGB-based depth estimation with a weighted geometric graph neural network (WG-GNN). Leveraging VideoDepthAnything to recover depth, the method embeds facial keypoints into 3D Euclidean space and constructs edge weights that combine Euclidean distances with surface coplanarity. An anatomy-aware scaled dot-product attention mechanism is introduced to facilitate geometric message passing, ultimately yielding a continuous, normalized pain score ranging from 0% to 100%. Requiring no specialized depth-sensing hardware, this approach substantially enhances the modeling of SPFES-relevant features and their 3D spatial dependencies, enabling highly accurate automated pain assessment.

Abstract

Deep learning systems perform mainly within the 2D for a single image domain and take the face as a single-dimension representation, losing sight of the 3D anatomy of sheep and cross-landmark spatial relationships that are intrinsic to the clinically proven Sheep Pain Facial Expression Scale (SPFES). This paper presents the \textbf{3D Sheep Pain Facial Expression System (3D-SPFES)}, a novel, monocular depth-aware geometric graph neural network system that integrates each SPFES facial landmark, such as the ears, eyes, and nose, into 3D Euclidean space estimated from a single RGB camera by using VideoDepthAnything, thus preventing the need for specialized depth hardware. Each landmark node includes a feature vector containing its 3D spatial coordinates, estimated surface normal, and facial attribute class embedding. Edges linked to nodes are assigned weights based on an aggregate metric that combines both Euclidean distance and surface co-planarity in a 3D space. A Weighted Geometric Graph Neural Network (WG-GNN) studies this graph using $\mathcal{K} = 3$ geometry-aware message-passing layers enhanced by a scaled dot-product attention method that selectively enhances anatomically relevant inter-landmark messages. The resultant node embeddings are combined into $\mathcal{O} = 3$ pain-level clusters and integrated into a Normalized Pain Score (NPS) within the range of $[0, 100%]$ a confidence-weighted, SPFES-derived scoring method.

Problem

• 3D facial pain assessment
• Sheep Pain Facial Expression Scale
• geometric graph neural networks
• monocular depth estimation
• spatial relationships

Innovation

• 3D Geometric Graph Neural Network
• Monocular Depth Estimation
• Weighted Graph Attention
• Sheep Pain Assessment
• Facial Landmark Embedding