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arxiv: 2606.12036 · v1 · pith:WQLQEAKSnew · submitted 2026-06-10 · 💻 cs.CV

Vision Transformers for Face Recognition Need More Registers

Tahar Chettaoui , Guray Ozgur , Eduarda Caldeira , Naser Damer , Fadi Boutros This is my paper

Pith reviewed 2026-06-27 09:45 UTC · model grok-4.3

classification 💻 cs.CV
keywords vision transformerface recognitionregister tokensattention artifactsconcatenated patch embeddingsinterpretabilityIJB-BIJB-C
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The pith

Register tokens added to CPE-based Vision Transformers reduce attention artifacts and reach state-of-the-art face recognition accuracy.

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

The paper establishes that Concatenated Patch Embeddings improve Vision Transformer performance for face recognition but introduce consistent attention map artifacts that reduce interpretability. Adding learnable register tokens to the patch embeddings mitigates these artifacts across model sizes, with eight registers yielding the clearest maps and best results. The resulting ViT-8R model sets new benchmarks on the large-scale IJB-B and IJB-C datasets. A sympathetic reader would care because the clearer attention structures provide direct insight into what facial regions the model uses for identity decisions.

Core claim

Incorporating register tokens into CPE-based ViT architectures for face recognition mitigates attention artifacts observed in baseline models, with eight registers delivering the highest verification accuracies on IJB-B and IJB-C and substantially clearer attention maps compared to the baseline CPE model.

What carries the argument

Register tokens: learnable tokens concatenated to the initial patch embeddings and processed jointly through the ViT encoder blocks.

If this is right

  • Adding four or eight registers significantly enhances interpretability of attention maps.
  • Eight registers provide the highest verification accuracies and smoothest attention structures.
  • The ViT-8R model achieves state-of-the-art performance among ViT-based FR models on IJB-B and IJB-C.
  • Artifacts appear consistently across small and large ViT backbones and are mitigated by registers.

Where Pith is reading between the lines

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

  • The register token method could be tested on other ViT tasks where attention interpretability matters, such as object detection.
  • The optimal number of registers may depend on input complexity, suggesting experiments with different counts per domain.
  • Clearer attention maps could aid in diagnosing biases within face recognition systems without extra post-processing.

Load-bearing premise

The accuracy gains and artifact reductions are caused by the register tokens rather than by any unstated differences in training procedure, augmentation, or hyperparameters.

What would settle it

Retrain the baseline CPE model using the exact same training procedure, data augmentation, and hyperparameters as the eight-register version, then check whether the attention artifacts remain at similar levels.

Figures

Figures reproduced from arXiv: 2606.12036 by Eduarda Caldeira, Fadi Boutros, Guray Ozgur, Naser Damer, Tahar Chettaoui.

Figure 1
Figure 1. Figure 1: Attention maps of CPE-based ViT-B for FR, [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: CPE-based ViT with Registers pipeline. The image [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: CPE-based attention maps, averaged across five benchmarks (see Section IV), of ViT-L, ViT-B, ViT-S, and ViT-T backbones for FR. All architectures display artifacts [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Attention map of the CLS token in ViT-B averaged across five benchmarks (see Section IV). The CLS-based ViT-B does not suffer from background artifacts. MS-Celeb-1M dataset [19], curated and refined by [16], and comprises 5.8M images of 85K identities. MS1MV3 is a cleaned and updated iteration of MS-Celeb-1M, containing 5.2M images of 96k identities. WebFace4M is a subset of WebFace260M [51], consisting of… view at source ↗
Figure 5
Figure 5. Figure 5: Attention maps of CPE-based ViT-B for FR, [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Attention maps of KP-RPE [29], averaged across five benchmarks (see Section 4), comparing models trained with 0, 1, 2, 4, and 8 registers. Similarly to ViT, we observe that artifacts are reduced once 4 or 8 registers are added. FLOPs and parameter count. The CPE-based methods con￾catenate all patch embeddings z i L ∈ R D for i = 1, . . . , N, which are then projected to a final feature vector. While the re… view at source ↗
read the original abstract

Recent advances in Vision Transformers (ViTs) for face recognition (FR) have moved beyond the standard CLS-token paradigm. In this paradigm, a special classification token (CLS) is prepended to the patch embeddings and used as a representation of the input for downstream tasks. An alternative approach, Concatenated Patch Embeddings (CPE), instead leverages all patch tokens by concatenating them into a single vector, which is then projected into a compact face representation. CPE has been shown to improve recognition performance in comparison to CLS-based ones, but our qualitative analysis of attention maps showed the presence of artifacts that limit their interpretability. To address this issue, we incorporate register tokens, learnable tokens concatenated to the initial patch embeddings, and processed jointly through the ViT encoder blocks. This mechanism has been shown to produce more structured and interpretable attention maps compared to baseline ViT. We empirically demonstrate that these artifacts consistently appear across various ViT backbones, including small and large models, and that introducing register tokens effectively mitigates them. Adding four or eight registers significantly enhances interpretability, with eight registers providing the highest verification accuracies and smoothest attention structures. Our resulting model, ViT-8R, corresponds to a CPE-based ViT-B architecture augmented with eight register tokens achieves state-of-the-art performance among ViT-based FR models on large-scale IJB-B and IJB-C benchmarks. Also, ViT-8R produces substantially clearer attention maps compared with the baseline model, which offer deeper insight into the model's attention behavior (https://github.com/TaharChettaoui/ViT-FR-Registers)

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 paper proposes adding learnable register tokens to Concatenated Patch Embeddings (CPE) Vision Transformers for face recognition to reduce attention artifacts and boost performance. It reports that 4–8 registers yield clearer attention maps across backbones and that the resulting ViT-8R (CPE ViT-B + 8 registers) achieves state-of-the-art verification accuracy among ViT-based models on the large-scale IJB-B and IJB-C benchmarks, with code released at the provided GitHub repository.

Significance. If the accuracy gains and artifact reduction are causally due to the registers, the work supplies a lightweight architectural change that simultaneously improves accuracy and interpretability for ViT-based face recognition; the public code release is a concrete strength that supports reproducibility.

major comments (2)
  1. [Experimental protocol / results comparison] The central empirical claim (abstract and results) that ViT-8R outperforms the CPE baseline and reaches SOTA rests on the assumption that the two models share identical training details (optimizer, learning-rate schedule, data augmentation, loss, and initialization). No explicit statement, table, or section confirms this matching; without it the performance delta cannot be attributed to the register tokens alone.
  2. [Attention map analysis] The qualitative claim that registers 'effectively mitigate' artifacts 'consistently across various ViT backbones' is load-bearing for the interpretability contribution, yet the manuscript provides no quantitative metric (e.g., attention entropy or artifact count) to support the visual examples; the improvement therefore remains qualitative only.
minor comments (1)
  1. [Abstract] The abstract states 'ViT-8R ... achieves state-of-the-art performance' but does not list the exact competing ViT-based methods or their scores; a compact comparison table would strengthen the SOTA claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major point below and will revise the paper to strengthen the presentation of our results.

read point-by-point responses

  1. Referee: [Experimental protocol / results comparison] The central empirical claim (abstract and results) that ViT-8R outperforms the CPE baseline and reaches SOTA rests on the assumption that the two models share identical training details (optimizer, learning-rate schedule, data augmentation, loss, and initialization). No explicit statement, table, or section confirms this matching; without it the performance delta cannot be attributed to the register tokens alone.

    Authors: We agree that an explicit confirmation of identical training protocols is required to attribute performance differences to the register tokens. All models in our experiments were trained with the same optimizer, learning-rate schedule, data augmentations, loss function, and initialization, with the sole difference being the addition of register tokens. In the revised manuscript we will add a dedicated paragraph in the Experimental Setup section that states this matching explicitly, along with a table summarizing the shared hyperparameters. revision: yes

  2. Referee: [Attention map analysis] The qualitative claim that registers 'effectively mitigate' artifacts 'consistently across various ViT backbones' is load-bearing for the interpretability contribution, yet the manuscript provides no quantitative metric (e.g., attention entropy or artifact count) to support the visual examples; the improvement therefore remains qualitative only.

    Authors: We acknowledge that the attention-map analysis is currently supported only by qualitative examples. While the visual results show consistent artifact reduction, we agree a quantitative metric would strengthen the claim. In the revision we will add a quantitative evaluation (e.g., mean attention entropy or a systematic count of artifact regions) computed on held-out images to complement the qualitative figures. revision: yes

Circularity Check

0 steps flagged

No circularity; purely empirical evaluation on public benchmarks

full rationale

The paper reports experimental results from training CPE-based ViT models augmented with register tokens and measuring verification accuracy plus attention-map quality on IJB-B and IJB-C. No derivation, equation, or prediction is presented that reduces by construction to a fitted parameter or self-citation chain defined inside the work. All central claims rest on externally falsifiable benchmark numbers and qualitative visualizations whose validity does not depend on any internal redefinition of the inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The improvement rests on the empirical selection of the number of registers and on the background claim from prior work that register tokens improve attention structure; no new entities are postulated.

free parameters (1)
  • Number of register tokens = 8
    The authors test four and eight registers and select eight because it yields the highest verification accuracies and smoothest attention structures.
axioms (1)
  • domain assumption Register tokens produce more structured and interpretable attention maps in Vision Transformers
    Invoked in the abstract as a known property shown in earlier ViT work and used to motivate the addition of registers to the CPE face-recognition pipeline.

pith-pipeline@v0.9.1-grok · 5838 in / 1398 out tokens · 21798 ms · 2026-06-27T09:45:32.945013+00:00 · methodology

discussion (0)

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