Papers › Multi-Label Compound Expression Recognition: C-EXPR Database & Network
Multi-Label Compound Expression Recognition: C-EXPR Database & Network
Dimitrios Kollias
Research in automatic analysis of facial expressions mainly focuses on recognising the seven basic ones. However, compound expressions are more diverse and represent the complexity and subtlety of our daily affective displays more accurately. Limited research has been conducted for compound expression recognition (CER), because only a few databases exist, which are small, lab controlled, imbalanced and static. In this paper we present an in-the-wild A/V database, C-EXPR-DB, consisting of 400 videos of 200K frames, annotated in terms of 13 compound expressions, valence-arousal emotion descriptors, action units, speech, facial landmarks and attributes. We also propose C-EXPR-NET, a multi-task learning (MTL) method for CER and AU detection (AU-D); the latter task is introduced to enhance CER performance. For AU-D we incorporate AU semantic description along with visual information. For CER we use a multi-label formulation and the KL-divergence loss. We also propose a distribution matching loss for coupling CER and AU-D tasks to boost their performance and alleviate negative transfer (i.e., when MT model's performance is worse than that of at least one single-task model). An extensive experimental study has been conducted illustrating the excellent performance of C-EXPR-NET, validating the theoretical claims. Finally, C-EXPR-NET is shown to effectively generalize its knowledge in new emotion recognition contexts, in a zero-shot manner.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Facial Expression Recognition (FER) | RAF-DB | C-EXPR-NET | Avg. Accuracy | 87.5 | #30 of 35 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections