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REVIEW 3 major objections 5 minor 1 cited by

Impact of Face Alignment on Face Image Quality

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper shows that face image quality assessment methods are sensitive to face alignment: cropped-only images receive systematically lower quality scores than landmark-aligned images, with the gap widening under challenging real-world…

desk verdict Useful empirical pattern, but the headline attribution to alignment is confounded by differences in scale, margins, and resampling; worth a careful revision. read the letter →

arxiv 2412.11779 v1 pith:H5HF7T3Q submitted 2024-12-16 cs.CV

Eren Onaran , Erdi Sar{i}taş , Haz{i}m Kemal Ekenel This is my paper · ORCID
classification cs.CV
keywords facealignmentimagequalityassessmentFIQAMTCNNRetinaSER-FIQSCFacesurveillancerecognition
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper asks whether the preprocessing step of face alignment changes the quality score a face image receives from a face image quality assessment (FIQA) method. The authors compare two versions of each detected face, one simply cropped from the detector's bounding box and one geometrically aligned using facial landmarks, across three datasets and four FIQA methods. They report that aligned faces consistently score higher and with less variability than cropped faces, and that the difference grows in low-quality surveillance images. If correct, FIQA evaluations that skip or vary alignment are not measuring image quality alone; they are also measuring alignment-induced degradation, so alignment must be controlled for scores to be comparable.

What carries the argument

The central object is the similarity transformation $T$ that maps five detected facial landmarks (eye corners, nose tip, mouth corners) onto the fixed ArcFace template, combining scaling, rotation, and translation while minimizing the sum of squared landmark distances. This transformation is what separates the 'Aligned' images from the 'Cropped' images in every comparison. The argument's force comes from pairing this transformation with four FIQA scorers and comparing the resulting score distributions and means, treating alignment as the independent variable.

What would settle it

A controlled experiment that fixes the crop scale, margins, and interpolation for both versions and varies only the landmark-based rotation and translation would settle the claim: if quality scores do not decrease when the face is geometrically rotated or translated relative to the template, then alignment itself is not the cause of the observed score drop.

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Extended reading notes

Core claim

The paper establishes that FIQA methods are alignment-sensitive: converting a detected face into a landmark-aligned image via a similarity transformation to the ArcFace template, instead of using the raw detector crop, yields higher and more consistent quality scores. This pattern holds across SER-FIQ, FaceQAN, DifFIQA, and SDD-FIQA on LFW and IJB-B, and it becomes more pronounced with distance on the surveillance dataset SCFace, where SER-FIQ mean scores at 4.2m roughly double or more after alignment (e.g., from 0.187 to 0.474 for MTCNN and from 0.070 to 0.426 for RetinaFace). The paper interprets these results as showing that misaligned face images produce lower quality scores, and it suggests the likely reason is that FIQA methods are trained on aligned images and therefore penalize inputs that deviate from that distribution.

Load-bearing premise

The two compared pipelines differ in more than alignment alone: MTCNN crops are scaled by 1.2, RetinaFace crops use raw bounding boxes, and alignment is applied to the original image, so scale, margins, interpolation, and resolution also differ between 'Cropped' and 'Aligned'; if those confounds rather than landmark geometry drive the score gap, the central claim is not established.

Editorial extensions

If this is right

  • FIQA scores from different studies are not directly comparable unless the alignment step is specified and controlled.
  • Quality gaps attributed to capture distance in surveillance scenarios may be partly caused by alignment failure rather than by resolution alone.
  • Deployment pipelines that use FIQA to filter or rank faces should align faces before scoring, or they will systematically undervalue unaligned faces.
  • FIQA method design should consider robustness to alignment as a separate axis, since training on aligned images can create an unintended sensitivity to preprocessing.
  • Benchmark conclusions about which FIQA method performs best could change if the preprocessing protocol were held fixed across methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the cropped and aligned versions in this study also differ in scale, margins, interpolation, and resolution (MTCNN boxes are scaled by 1.2, RetinaFace boxes are used directly, and alignment is applied to the original image), the measured gap is an upper bound on the pure alignment effect; a controlled study holding crop dimensions fixed while varying only landmark-based rotation and transl
  • The same sensitivity likely carries into FIQA-driven face recognition pipelines: if quality scores are used to discard low-quality faces before recognition, alignment errors could cause recognizable faces to be discarded as low quality.
  • A practical takeaway not stated by the authors is that standardizing the alignment template before scoring could reduce FIQA variance on low-quality surveillance data at near-zero computational cost relative to the recognition step.
  • Future FIQA benchmarking could treat alignment as a protocol parameter, reporting scores for both cropped and aligned versions so that robustness to preprocessing becomes a measurable property.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper empirically compares FIQA scores computed on 'Cropped' faces versus 'Aligned' faces across three datasets (LFW, IJB-B, SCFace), two detectors (MTCNN, RetinaFace), and four FIQA methods (SER-FIQ, FaceQAN, DifFIQA, SDD-FIQA). The authors report that aligned images consistently receive higher quality scores than cropped ones, with the gap larger for SCFace at longer distances, and conclude that FIQA methods are sensitive to alignment and that misaligned (cropped-only) faces yield lower quality scores.

Significance. If the central claim is established, the paper would be a useful cautionary note for the FIQA community: quality scores are not comparable across preprocessing pipelines unless alignment is standardized, and alignment errors could be an additional quality-degradation factor in surveillance scenarios. The paper covers a relevant combination of detectors and state-of-the-art FIQA models and presents results on three standard benchmarks. However, the current experimental design does not isolate alignment as the causal factor, so the main conclusion remains unsubstantiated as stated.

major comments (3)
  1. [Section 3.3] The 'Cropped' and 'Aligned' conditions differ in several properties beyond geometric alignment. For MTCNN, the bounding box is scaled by 1.2 before cropping, while RetinaFace boxes are used directly; the aligned images are produced by a similarity transformation (Eqs. 1–3) applied to the original image with the ArcFace template. As a result, the two sets differ in output size, face scale within the frame, margins, interpolation/resampling, and potentially resolution. Any of these confounds could drive the observed score gap, especially because the four evaluated FIQA models were trained on aligned images. The authors should compare conditions that vary only the alignment transformation while keeping scale, margins, and resampling fixed—for example, by aligning to the template and then cropping with identical margins, or by applying a fixed similarity transform with perturbed landmark targets to the same crop. Without such a matched-pair control, the paper's central claim that 'FIQA methods are sensitive to alignment' is not established.
  2. [Section 5, Figs. 4–5, Table 1] The results are presented as aggregated distributions and mean scores without any measure of uncertainty or statistical testing. The paper reports no standard deviations, confidence intervals, or per-pair comparisons, and does not state the number of images used for each distribution. Since the claim is a systematic score difference, the authors should report paired differences (cropped vs. aligned for the same identity and image), effect sizes, and significance tests (e.g., Wilcoxon signed-rank or bootstrap confidence intervals). This is particularly important because the qualitative reading of Fig. 4 and Fig. 5 may depend on kernel density smoothing choices, and Table 1 reports only means. Without this, the reader cannot judge whether the observed gaps are reliable or within sampling variability.
  3. [Section 5.1 and Section 6] The Discussion acknowledges that 'the FIQA methods were trained using aligned face images,' but this fact is also a potential full explanation for the observed gap: the tested quality models may simply assign lower scores to any input that differs from their training distribution, regardless of whether the difference is due to alignment or to the crop/margin/resampling confounds identified above. To support the paper's interpretation, the authors should include a control experiment that decouples alignment from the training distribution—for example, using a FIQA model trained on unaligned crops (if available) or evaluating a generic image-quality metric (e.g., BRISQUE or NIQE) that does not depend on aligned face training. The current wording in Section 6, 'misaligned face images – only cropped versions – produce lower quality scores than properly aligned ones,' overstates what the experiments can distinguish.
minor comments (5)
  1. [Section 3.3] The paper uses 'misaligned' to refer to the cropped-only images, but a tightly cropped face is not necessarily misaligned; the term should be defined more precisely, e.g., as 'not geometrically normalized by a landmark-based transform.'
  2. [Fig. 3] The sample images and the FIQA scores printed below them are too small to be readable in the provided figure; the authors should enlarge the images or present the scores in a separate table for the samples.
  3. [Section 5] The text says 'the quality score distributions of aligned images are very similar, whether MTCNN or RetinaFace is used,' but no quantitative similarity measure is given; a simple two-sample test or distribution-distance value would support this statement.
  4. [References] There are minor citation inconsistencies, e.g., 'GraFIQs' is listed as [14] in the background but the style of the reference list is not uniform; also, the citation to the SCFace database [10] is a journal article while others are conference papers, which is fine, but the caption of Table 1 should restate the N per cell (the paper says SCFace contains 15 images per subject, but the table reports averages with no count).
  5. [Section 5.1] The sentence 'This much quality drop when using the cropped images still remains unexpected' is informal; it would be clearer to state the expected drop under a specific hypothesis and compare the observed magnitude to that expectation.

Circularity Check

0 steps flagged · score 2.0 of 10

No formal circularity: the paper is an empirical comparison with external FIQA models and datasets, and no predicted quantity is fitted from the data. The small score reflects the authors' own acknowledged caveat that the quality models were trained on aligned faces, which is a measurement bias rather than a circular derivation.

full rationale

No circular step can be exhibited from the paper's equations or citations. The central result, that cropped (unaligned) face images receive lower FIQA scores than aligned ones, is obtained by running four externally published FIQA models (SER-FIQ, FaceQAN, DifFIQA, SDD-FIQA) on three standard benchmarks (LFW, IJB-B, SCFace); the paper fits no parameter to the data and then renames it as a prediction. The alignment transformation in Section 3.3 (Eqs. 1-3) is a standard similarity warp to the ArcFace template, and the comparison is descriptive rather than derived. The only self-citations ([20] for combined-degradation amplification and [8,7] for alignment importance in face recognition) are supporting background statements and are not load-bearing for the reported statistics. The Discussion explicitly concedes the main limitation: 'Similar to face recognition [19,1], this behaviour may result from the FIQA methods being trained with aligned images.' Because SER-FIQ is described as 'based on the ArcFace model' and the aligned condition is built with the ArcFace template, part of the score gap is plausibly an artifact of the instruments' training distribution rather than an intrinsic property of alignment; however, the authors acknowledge this, and it is a bias/confound, not a circular reduction. A separate concern is that the 'Cropped' and 'Aligned' conditions differ in box scaling, margins, and resampling (MTCNN boxes scaled by 1.2, RetinaFace boxes used directly, ArcFace similarity warp), which weakens the causal attribution but is a correctness risk, not circularity. Overall, the derivation chain is self-contained and the low score reflects only the acknowledged instrument-training caveat and minor non-load-bearing self-citations.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claim relies on the assumption that FIQA scores measure quality and that observed differences are caused by alignment. The experiment uses two hand-chosen preprocessing parameters, the MTCNN box expansion and the ArcFace alignment template, plus three unstated domain assumptions. No new entities are postulated.

free parameters (2)
  • MTCNN bounding box scale factor = 1.2
    Hand-chosen expansion of MTCNN boxes before cropping; changes the crop content relative to aligned images and therefore affects the comparison.
  • ArcFace alignment template coordinates = not specified in paper
    Target landmark positions and output size inherited from ArcFace (Reference 6); choices affect how strongly images are warped and could influence quality scores.
assumptions (3)
  • domain assumption FIQA scores are meaningful indicators of face image quality for downstream recognition.
    The paper interprets higher mean scores as better quality without validating against recognition performance or human perception.
  • domain assumption Differences in FIQA score distributions between cropped and aligned images are attributable to alignment, not to other image transformations.
    Section 3.3 pipelines differ in scale, margin, and interpolation; causal attribution to alignment is assumed.
  • domain assumption Detected landmarks are accurate enough for alignment, and alignment errors are caused by image degradation.
    Discussion in Section 5.1 infers that low quality causes faulty landmark localization, but landmark accuracy is not measured.

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Cite this review

Pith. "Pith review of Impact of Face Alignment on Face Image Quality." pith.science (2026). https://pith.science/paper/H5HF7T3Q

@misc{pith2026241211779,
  author       = {Pith},
  title        = {Pith review of: Impact of Face Alignment on Face Image Quality},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H5HF7T3Q}},
  note         = {Machine review of arXiv:2412.11779}
}
read the original abstract

Face alignment is a crucial step in preparing face images for feature extraction in facial analysis tasks. For applications such as face recognition, facial expression recognition, and facial attribute classification, alignment is widely utilized during both training and inference to standardize the positions of key landmarks in the face. It is well known that the application and method of face alignment significantly affect the performance of facial analysis models. However, the impact of alignment on face image quality has not been thoroughly investigated. Current FIQA studies often assume alignment as a prerequisite but do not explicitly evaluate how alignment affects quality metrics, especially with the advent of modern deep learning-based detectors that integrate detection and landmark localization. To address this need, our study examines the impact of face alignment on face image quality scores. We conducted experiments on the LFW, IJB-B, and SCFace datasets, employing MTCNN and RetinaFace models for face detection and alignment. To evaluate face image quality, we utilized several assessment methods, including SER-FIQ, FaceQAN, DifFIQA, and SDD-FIQA. Our analysis included examining quality score distributions for the LFW and IJB-B datasets and analyzing average quality scores at varying distances in the SCFace dataset. Our findings reveal that face image quality assessment methods are sensitive to alignment. Moreover, this sensitivity increases under challenging real-life conditions, highlighting the importance of evaluating alignment's role in quality assessment.

Figures

Figures reproduced from arXiv: 2412.11779 by the authors.

Figure 1
Figure 1. The general overview of our pipeline is as follows. A face detector first [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Sample original images from LFW [13], IJB-B [26], and SCFace [10]. [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Sample original images from LFW [13], IJB-B [26], and SCFace [10], their [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Quality score distributions for LFW [13] dataset. [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: Quality score distributions for IJB-B [26] dataset. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

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Forward citations

Cited by 1 Pith paper

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