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arxiv: 2606.20823 · v1 · pith:B77QDTB7new · submitted 2026-06-18 · 💻 cs.CV

NeoLoc-68: End-to-end 68-point neonatal facial landmark localisation in neonatal clinical environments

Abdullah Bin-Obaid , Maria M. Cobo , Rebeccah Slater , Lionel Tarassenko , Mauricio Villarroel This is my paper

Pith reviewed 2026-06-26 18:11 UTC · model grok-4.3

classification 💻 cs.CV
keywords facial landmark detectionneonatal facesclinical environments68-point landmarksYOLO keypoint modelpain assessmentgeneralizationend-to-end detection
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The pith

Mixing standardized adult face images with neonatal frames trains the first end-to-end model to locate 68 landmarks on babies in clinical settings.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a detector that places 68 specific points on newborn faces even when the infants are in hospital environments surrounded by equipment and showing sudden movements. It standardizes over 37,000 images from public adult datasets to a common landmark scheme and combines them with over 1,000 manually labeled neonatal frames to train a regression model. The system starts from weights of a neonatal face detector and uses a keypoint approach to output the points directly. This step matters because accurate landmarks form the basis for automated systems that read facial expressions to judge pain without touching the baby. If the approach holds, it provides a foundation for contact-free monitoring tools that clinicians could use in neonatal care.

Core claim

The paper claims that standardizing 37,459 single-face images from 11 public datasets to a 68-point markup and mixing them with 1,123 annotated neonatal frames produces a training set that lets an adapted YOLO-based keypoint model regress 68 landmarks on neonatal faces in clinical conditions, delivering the lowest detection failure rate among tested baselines on a clinical test set before fine-tuning and further gains after fine-tuning, establishing the first end-to-end 68-point neonatal model.

What carries the argument

The YOLO-based keypoint model adapted to regress 68 facial landmarks, initialized with weights from a pretrained neonatal face detector.

If this is right

  • The model reaches state-of-the-art numbers on public face datasets using the standardized 68-point markup.
  • It generalizes to clinical neonatal images with the lowest failure rate among baselines even before any neonatal fine-tuning.
  • Fine-tuning on additional neonatal frames further lowers error rates and failure rates.
  • The outputs can feed directly into downstream neonatal health monitoring and facial expression analysis tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar mixing of public adult data with small specialized sets could reduce the need for large new annotations in other medical imaging domains with limited samples.
  • The landmark outputs could support video-based tracking of expression changes over time rather than single frames.
  • Deployment in neonatal units might allow continuous non-contact observation of distress signals during routine care.

Load-bearing premise

That standardizing adult images to a shared 68-point scheme and mixing them with limited neonatal frames creates a training distribution whose gap to real clinical neonatal images remains small enough for the observed generalization to hold.

What would settle it

A fresh clinical neonatal test set annotated by separate experts on which the model shows detection failure rates substantially above the reported levels would show the generalization claim does not hold.

Figures

Figures reproduced from arXiv: 2606.20823 by Abdullah Bin-Obaid, Lionel Tarassenko, Maria M. Cobo, Mauricio Villarroel, Rebeccah Slater.

Figure 1
Figure 1. Figure 1: The 68-point facial landmark annotation scheme and example annotations. The 68-point facial landmark configuration and an example annotation. (a) The landmarks are numbered in order and span the key facial regions, including the jawline, eyebrows, eyes, nose, and mouth (image adapted from [17]). (b) Example image showing landmark annota￾tions (green), the landmark-derived bounding box (red), the expanded g… view at source ↗
Figure 2
Figure 2. Figure 2: Example images from the selected facial landmark datasets used in this work. Each image is accompanied by its dataset name on the left and is shown with its corresponding set of 68 annotated facial landmarks, reformatted where necessary to follow the iBUG markup scheme. Images have been cropped around the face region to provide consistent framing and improve visual comparability across datasets. 9 [PITH_F… view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of the weight transfer process from the detection model to the key￾points model. The high-level architectures of both models: the face detection model (top) and the keypoints model (bottom). Both share the same backbone (dark blue rectangle), neck (light blue rectangle), and detection heads (green rectangles). The keypoints model includes additional regressor heads (gold rectangles) for keypoi… view at source ↗
read the original abstract

Facial landmark localisation is a prerequisite for developing automated, non-contact neonatal pain assessment methods. Clinicians use pain scales to judge the severity of pain, many of which rely on facial expression. However, facial landmark detectors trained on adult faces perform poorly in neonatal clinical environments due to frequent occlusions caused by medical equipment, varied head poses, and challenging imaging conditions, including motion blur triggered by sudden pain-related movements. We propose an end-to-end facial landmark detector capable of predicting 68 landmarks on neonatal faces in clinical environments. We combined 37,459 single-face images from 11 public datasets, standardised to 68-point markup, with 1,123 manually annotated frames from a neonatal research dataset (totalling over 76,000 landmarks). A YOLO-based keypoint model was adapted to regress the facial landmarks, initialised with weights from a pretrained neonatal face detector. On public datasets, our proposed model achieved state-of-the-art performance: Normalised Mean Error (NME) = 5.37, Failure Rate (FR) = 12.5%, Area Under the Cumulative Error Curve (AUC) at AUC0.08 = 38.00% and AUC0.1 = 48.70%. On the clinical neonatal test set, before fine-tuning, the model achieved the lowest Detection Failure Rate (DFR) = 5.3% among all baselines and showed strong generalisation. After fine-tuning, performance improved further to NME = 6.36, FR = 22.30%, DFR = 1.77%, AUC0.08 = 29.24% and AUC0.1 = 40.25%. To the best of our knowledge, this represents the first end-to-end 68-point neonatal facial landmark detection model. With further dataset expansion and refinement, it could support downstream tasks in neonatal health monitoring and pain-related facial analysis.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces NeoLoc-68, claimed as the first end-to-end 68-point facial landmark detector for neonatal faces in clinical environments. It trains a YOLO-based keypoint regressor on 37,459 images from 11 public (mostly adult) datasets standardized to 68-point markup plus 1,123 manually annotated neonatal frames, initialized from pretrained neonatal face detector weights. Reported results include SOTA on public datasets (NME=5.37, FR=12.5%, AUC0.08=38.00%) and strong generalization on a held-out clinical neonatal test set (DFR=5.3% before fine-tuning; after fine-tuning NME=6.36, FR=22.30%, DFR=1.77%).

Significance. If the generalization results hold after addressing verification gaps, the work would be significant for enabling non-contact neonatal pain assessment via facial expression analysis under real clinical conditions (occlusions, motion blur, varied poses). The mixed-dataset training strategy and multi-metric evaluation (including DFR and AUC) are positive aspects; however, the absence of protocol details limits immediate utility for downstream tasks.

major comments (2)
  1. [Data section] Data section: The standardization of 37,459 images from 11 heterogeneous public datasets to a common 68-point markup is described at a high level but supplies no explicit landmark mapping, inter-annotator agreement, or geometric consistency checks against neonatal anatomy. This is load-bearing for the central generalization claim (DFR=5.3% on unseen clinical neonatal frames before fine-tuning), as systematic offsets from adult-to-68 conversion could be absorbed into the regressor without true domain transfer.
  2. [Experiments section] Experiments section: The manuscript reports numeric performance metrics, SOTA claims, and baseline comparisons but provides no experimental protocol, train-test split information, baseline implementation details, or error analysis. This prevents verification of the reported results (e.g., pre-fine-tune DFR=5.3% and post-fine-tune metrics) and undermines the soundness of the generalization statements.
minor comments (1)
  1. [Abstract] Abstract: The parenthetical '(totalling over 76,000 landmarks)' appears to count only the 1,123 neonatal frames (≈76k landmarks) while omitting the contribution from the 37,459 public images; this is a minor arithmetic/presentation inconsistency.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important areas for improving the clarity and verifiability of our work. We address each major comment point-by-point below and commit to revisions that will strengthen the manuscript without altering its core claims.

read point-by-point responses

  1. Referee: [Data section] The standardization of 37,459 images from 11 heterogeneous public datasets to a common 68-point markup is described at a high level but supplies no explicit landmark mapping, inter-annotator agreement, or geometric consistency checks against neonatal anatomy. This is load-bearing for the central generalization claim (DFR=5.3% on unseen clinical neonatal frames before fine-tuning), as systematic offsets from adult-to-68 conversion could be absorbed into the regressor without true domain transfer.

    Authors: We acknowledge that the Data section currently provides only a high-level description of the standardization process. To directly address this concern and support the generalization results, the revised manuscript will include an expanded Data section with explicit per-dataset landmark mappings to the 68-point format, available inter-annotator agreement statistics for the manually annotated neonatal frames, and a description of the geometric consistency checks applied during standardization to align with neonatal anatomy. These additions will clarify that the reported pre-fine-tuning DFR reflects genuine domain transfer rather than absorbed offsets. revision: yes

  2. Referee: [Experiments section] The manuscript reports numeric performance metrics, SOTA claims, and baseline comparisons but provides no experimental protocol, train-test split information, baseline implementation details, or error analysis. This prevents verification of the reported results (e.g., pre-fine-tune DFR=5.3% and post-fine-tune metrics) and undermines the soundness of the generalization statements.

    Authors: We agree that the absence of detailed experimental protocol information limits independent verification. In the revised manuscript, we will add a new Experimental Protocol subsection that specifies the train-test splits used for the public datasets and the held-out neonatal test set, full implementation details and hyperparameters for all baselines, the exact training procedure including initialization from the neonatal face detector, and an error analysis with qualitative examples of failure cases. This will enable full reproduction and verification of all reported metrics, including the pre- and post-fine-tuning DFR, NME, FR, and AUC values. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical evaluation on held-out sets

full rationale

The paper reports an ML training and evaluation pipeline for a YOLO-based keypoint regressor on a mixed adult+neonatal dataset, with performance measured via standard metrics (NME, FR, DFR, AUC) on explicitly held-out clinical neonatal test frames. No equations, derivations, or 'predictions' appear that reduce to fitted parameters or self-definitions by construction. No load-bearing self-citations or uniqueness theorems are invoked. The central claims rest on external test-set results rather than any internal redefinition or renaming of inputs, satisfying the self-contained criterion.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Central claim rests on transfer from mixed public datasets to neonatal clinical images; specific free parameters and assumptions are not detailed beyond the dataset counts and initialization choice.

free parameters (1)
  • pretrained neonatal face detector weights
    Model is initialised with weights from a pretrained neonatal face detector; this choice directly affects starting point and reported generalization.
axioms (1)
  • domain assumption Public adult face datasets can be standardized to the same 68-point markup and combined with neonatal frames without introducing systematic label noise or domain mismatch that invalidates clinical generalization
    The paper combines 37,459 images from 11 public datasets standardised to 68-point markup with 1,123 neonatal frames.

pith-pipeline@v0.9.1-grok · 5905 in / 1296 out tokens · 38022 ms · 2026-06-26T18:11:20.107289+00:00 · methodology

discussion (0)

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Reference graph

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