mlsanityData to 7 Oct 2026

Exp-Graph: How Connections Learn Facial Attributes in Graph-based Expression Recognition

arXiv 2507.14608 ↗First posted 19 Jul 2025cs.CV cs.AI cs.LGcited by 2

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Abstract (arXiv)

Facial expression recognition is crucial for human-computer interaction applications such as face animation, video surveillance, affective computing, medical analysis, etc. Since the structure of facial attributes varies with facial expressions, incorporating structural information into facial attributes is essential for facial expression recognition. In this paper, we propose Exp-Graph, a novel framework designed to represent the structural relationships among facial attributes using graph-based modeling for facial expression recognition. For facial attributes graph representation, facial landmarks are used as the graph's vertices. At the same time, the edges are determined based on the proximity of the facial landmark and the similarity of the local appearance of the facial attributes encoded using the vision transformer. Additionally, graph convolutional networks are utilized to capture and integrate these structural dependencies into the encoding of facial attributes, thereby enhancing the accuracy of expression recognition. Thus, Exp-Graph learns from the facial attribute graphs highly expressive semantic representations. On the other hand, the vision transformer and graph convolutional blocks help the framework exploit the local and global dependencies among the facial attributes that are essential for the recognition of facial expressions. We conducted comprehensive evaluations of the proposed Exp-Graph model on three benchmark datasets: Oulu-CASIA, eNTERFACE05, and AFEW. The model achieved recognition accuracies of 98.09%, 79.01%, and 56.39%, respectively. These results indicate that Exp-Graph maintains strong generalization capabilities across both controlled laboratory settings and real-world, unconstrained environments, underscoring its effectiveness for practical facial expression recognition applications.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 86 references that are in the map. First posted on arXiv 19 Jul 2025.

Builds on

32 of 86 references.

References · 32

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20 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Cranio-ID: Graph-Based Craniofacial Identification via Automatic Landmark Annotation in 2D Multi-View X-rayscited by 1
  2. 2026STAG: Spatio-temporal Evolving Structural Representation of Action Units for Micro-expression Recognitioncited by 0