Evidence map›Paper›PMID 42768459›Full record

ArticlePsychophysiology2026

blenderFaceUV: Toward Marker-Based Precision and Flexibility in Facial Tracking With "Invisible" Markers.

Axel Zinkernagel, Rainer W Alexandrowicz, Dennis Küster, Manfred Schmitt, Tanja Lischetzke

Abstract read
In one paragraph

Article in Psychophysiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Axel ZinkernagelUniversity of Wuppertal, Wuppertal, Germany.ORCID https://orcid.org/0000-0003-0404-1195
Rainer W AlexandrowiczUniversity of Klagenfurt, Klagenfurt, Austria.ORCID https://orcid.org/0000-0001-9846-8936
Dennis KüsterUniversity of Bremen, Bremen, Germany.ORCID https://orcid.org/0000-0001-8992-5648
Manfred SchmittRPTU University of Kaiserslautern-Landau, Landau, Germany.ORCID https://orcid.org/0000-0002-9978-8357
Tanja LischetzkeRPTU University of Kaiserslautern-Landau, Landau, Germany.ORCID https://orcid.org/0000-0002-4326-5127

Funding

Deutsche Forschungsgemeinschaft SCHM 1092/16-1
6 · The paper itself

Abstract

blenderFace is a highly precise marker-based approach for measuring facial activity. Unlike most camera-based automated facial expression recognition (AFER) systems, which rely on fixed natural landmarks, blenderFace flexibly tracks virtually any point of interest using painted markers. However, visible markers, like EMG electrodes, may alter appearance, increase self-awareness, and affect spontaneous responding in face-to-face interactions. To address these limitations, we introduce blenderFaceUV, which uses sunscreen-based markers that are visible only under ultraviolet (UV) light (365 nm). In Study 1, we assessed UV-marker tracking accuracy using parallel visible-light and UV recordings from two participants. In Study 2, we examined the scalability of blenderFaceUV in N = 18 lay participants prompted to produce a broad range of facial movements using common emotion terms. We also compared identical facial movements measured with blenderFaceUV and OpenFace, a widely used AFER system based on a point distribution model (PDM). Study 1 showed that UV markers could be tracked with similarly high accuracy as visible markers (reprojection/solve error < 0.3 pixels). Across 10 markers and two participants, with more than 3800 measurement occasions per participant, the mean correlation between visible and UV marker movements across the x- and y-coordinates was r = 0.957, while centered RMSE values indicated average deviations of approximately 1 mm. Study 2 demonstrated the scalability of the method and revealed PDM-related artifacts in OpenFace measurements, particularly during eye blinks and large mouth movements. PDM-interpolated landmarks, such as upper-lip markers, also deviated substantially from marker-based blenderFaceUV measurements. Practical challenges remain, including the complexity of applying invisible markers and the relatively high cost of UV cameras (≈$2000-$6000). We discuss technical requirements, limitations, and a recommended workflow for implementing blenderFaceUV.

Indexed as

Automated Facial RecognitionFacial ExpressionFacial RecognitionAdultEmotionsFaceFemaleHumansMaleMotion CaptureUltraviolet RaysYoung AdultblenderFacefacial movement measurementultraviolet lightUV recordingvideo recording

Identifiers

PMID42768459
PMCPMC13594373

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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.