ReviewSensors (Basel, Switzerland)2023
Understanding Naturalistic Facial Expressions with Deep Learning and Multimodal Large Language Models.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- blenderFaceUV: Toward Marker-Based Precision and Flexibility in Facial Tracking With "Invisible" Markers.Psychophysiology · 2026Article
- POC Sensor Systems and Artificial Intelligence-Where We Are Now and Where We Are Going?Biosensors · 2025Review
- A Review of 25 Spontaneous and Dynamic Facial Expression Databases of Basic Emotions.Affective science · 2025Review
- TPRO-NET: an EEG-based emotion recognition method reflecting subtle changes in emotion.Scientific reports · 2024Article
- Portable Facial Expression System Based on EMG Sensors and Machine Learning Models.Sensors (Basel, Switzerland) · 2024Article
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 2024Review
- Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?Cancers · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper provides a comprehensive overview of affective computing systems for facial expression recognition (FER) research in naturalistic contexts. The first section presents an updated account of user-friendly FER toolboxes incorporating state-of-the-art deep learning models and elaborates on their neural architectures, datasets, and performances across domains. These sophisticated FER toolboxes can robustly address a variety of challenges encountered in the wild such as variations in illumination and head pose, which may otherwise impact recognition accuracy. The second section of this paper discusses multimodal large language models (MLLMs) and their potential applications in affective science. MLLMs exhibit human-level capabilities for FER and enable the quantification of various contextual variables to provide context-aware emotion inferences. These advancements have the potential to revolutionize current methodological approaches for studying the contextual influences on emotions, leading to the development of contextualized emotion models.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.