Time to retire F1-binary score for action unit detection.

Action units Data imbalance F1 score Machine learning

Journal

Pattern recognition letters
ISSN: 0167-8655
Titre abrégé: Pattern Recognit Lett
Pays: Netherlands
ID NLM: 9879988

Informations de publication

Date de publication:
Jun 2024
Historique:
medline: 1 8 2024
pubmed: 1 8 2024
entrez: 1 8 2024
Statut: ppublish

Résumé

Detecting action units is an important task in face analysis, especially in facial expression recognition. This is due, in part, to the idea that expressions can be decomposed into multiple action units. To evaluate systems that detect action units, F1-binary score is often used as the evaluation metric. In this paper, we argue that F1-binary score does not reliably evaluate these models due largely to class imbalance. Because of this, F1-binary score should be retired and a suitable replacement should be used. We justify this argument through a detailed evaluation of the negative influence of class imbalance on action unit detection. This includes an investigation into the influence of class imbalance in train and test sets and in new data (i.e., generalizability). We empirically show that F1-micro should be used as the replacement for F1-binary.

Identifiants

pubmed: 39086494
doi: 10.1016/j.patrec.2024.04.016
pmc: PMC11290352
doi:

Types de publication

Journal Article

Langues

eng

Pagination

111-117

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Saurabh Hinduja (S)

Department of Psychology, University of Pittsburgh, Pittsburgh, USA.

Tara Nourivandi (T)

Department of Computer Science and Engineering, University of South Florida, USA.

Jeffrey F Cohn (JF)

Department of Psychology, University of Pittsburgh, Pittsburgh, USA.

Shaun Canavan (S)

Department of Computer Science and Engineering, University of South Florida, USA.

Classifications MeSH