ArticleFrontiers in neurorobotics2025
Emotion estimation from video footage with LSTM.
Article in Frontiers in neurorobotics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emotion estimation is a field that has been studied for a long time, and several approaches using machine learning models exist. This article presents BlendFER-Lite, an LSTM model that uses Blendshapes from the MediaPipe library to analyze facial expressions detected from a live-streamed camera feed. This model is trained on the FER2013 dataset and achieves 71% accuracy and an F1-score of 62%, meeting the accuracy benchmark for the FER2013 dataset while significantly reducing computational costs compared to current methods. For the sake of reproducibility, the code repository, datasets, and models proposed in this paper, in addition to the preprint, can be found on Hugging Face at: https://huggingface.co/papers/2501.13432. JEL Classification: D8, H51. MSC Classification: 35A01, 65L10, 65L12, 65L20, 65L70.
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.