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REVIEW 5 major objections 5 minor 97 references

A Bidirectional Siamese Recurrent Neural Network for Accurate Gait Recognition Using Body Landmarks

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

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Pith's one-line read A Siamese biGRU-dualStack network identifies people from six frames of 33 body landmarks, reporting 95.7% rank-1 accuracy on CASIA-B.

desk verdict Plausible lightweight gait recognizer whose headline accuracies are not yet checkable because the evaluation protocol is mostly unspecified. read the letter →

arxiv 2412.03498 v2 pith:LQ7PGY3D submitted 2024-12-04 cs.CV cs.AIcs.LG

Proma Hossain Progga , Md. Jobayer Rahman , Swapnil Biswas , Md. Shakil Ahmed , Arif Reza Anwary , Swakkhar Shatabda This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords gaitrecognitionbodylandmarksposeestimationProcrustesanalysisSiamesenetworkbidirectionalGRUcross-viewbiometricidentification
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

The paper aims to show that a person's identity can be recognized from a very short window of body landmarks: six frames of 33 landmark points, aligned by Procrustes analysis and processed by a Siamese network of two stacked bidirectional GRUs. If the reported accuracies hold, gait becomes a practical biometric at a distance, requiring only a few pose estimates rather than full silhouettes or dense 3D body models. The authors report rank-1 accuracies of 95.7% on CASIA-B, 94.44% on SZU RGB-D, 87.71% on OU-MVLP, and 86.6% on Gait3D. The ablation study shows the dual-stack bidirectional GRU outperforms RNN, LSTM, GRU, and single-direction variants on both SZU and CASIA-B.

What carries the argument

The load-bearing machinery is the combination of a sparse pose representation and a paired sequence encoder. Each person is represented as a 6 x 33 x 3 tensor of landmarks: six time steps, 33 landmark points, and x/y/z coordinates. Procrustes analysis, specifically generalized Procrustes alignment, removes rigid-body differences among subjects and viewpoints before the sequence reaches the network. The Siamese biGRU-dualStack architecture then encodes both forward and backward temporal context with two stacked bidirectional GRU layers per branch, concatenates the two branch outputs, passes them through a 1x1 dense layer and a sigmoid, and is trained with contrastive loss. The claim is that this compact representation captures identity-bearing gait dynamics without needing silhouettes or dense 3D models.

What would settle it

Run the exact pipeline on CASIA-B with six frames selected uniformly at random instead of by the left-foot x-extremum rule, keeping everything else identical. If rank-1 accuracy at 90 degrees falls well below the reported 96.1%, the result is driven by the cycle-spanning frame selection; if it stays about the same, the sparse window itself, not the cycle heuristic, carries the identity signal.

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Extended reading notes

Core claim

The paper's central claim is that spatial alignment of sparse landmark sequences plus bidirectional recurrent encoding is enough for competitive gait recognition across indoor and in-the-wild datasets. Concretely, the pipeline extracts six frames per gait sample, selects them so the left-foot landmark's x-coordinate goes from most negative to most positive (intended to span a full walking cycle), collects 33 landmarks per frame, aligns the configurations with Procrustes analysis to remove translation, rotation, and scale differences across viewpoints, and feeds the resulting 594-value sequence into a Siamese network whose branches each contain two stacked bidirectional GRUs with 128 units. A contrastive loss trains the network to pull same-person pairs together and push different-person pairs apart. The authors report that this beats prior model-based and appearance-based methods on SZU, OU-MVLP, and Gait3D and is competitive on CASIA-B.

Load-bearing premise

The result depends on the assumption that six frames chosen by watching the left-foot landmark's x-coordinate move from its most negative to its most positive value actually span a complete gait cycle, so the network sees the whole walking rhythm and not a partial or viewpoint-distorted fragment.

Editorial extensions

If this is right

  • Person identification can be performed from as few as six pose frames, so surveillance cameras with modest frame rates could support gait-based re-identification at a distance.
  • The approach generalizes across contrasting datasets (indoor, RGB-D, multi-view large population, and in-the-wild), suggesting the method is not tied to a single capture setup.
  • Landmark reduction from 33 to lower-body-only landmarks (23-32) costs little accuracy, so cheaper or simpler pose extraction may be feasible in practice.
  • The ablation implies that bidirectional, stacked recurrent encoding is the ingredient that matters most; simpler recurrent variants lose several points on both SZU and CASIA-B.

Reading between the lines

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

  • The paper implicitly treats the six-frame left-foot-extremum rule as a universal gait-cycle surrogate; a testable extension would compare this heuristic against ground-truth cycle segmentation across viewpoints and walking speeds.
  • The strong Gait3D result (86.6% versus 64.6% for the GaitBase baseline) suggests landmark sparsity plus alignment may transfer to unconstrained settings better than silhouette methods; ablating Procrustes alignment on Gait3D would reveal how much of that gain comes from alignment itself.
  • Because the input is only 594 values per sample, the model is plausibly fast enough for real-time edge deployment, but the paper does not report inference latency, so measuring it would be a natural next step.
  • The contrastive pairing strategy (400 pairs for CASIA-B and SZU, a 1:2 positive-negative ratio for Gait3D) is chosen without a sensitivity analysis; varying pair count and ratio could show whether performance depends on this choice.
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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

5 major / 5 minor

Summary. The paper proposes a gait recognition pipeline that extracts 33 Mediapipe body landmarks from video frames, selects six frames based on the left-foot landmark's x-coordinate, aligns the landmarks with Procrustes analysis, and feeds the resulting sequences into a Siamese network of two stacked bidirectional GRUs trained with a contrastive loss. The authors report Rank-1 accuracies of 95.7% on CASIA-B (NM condition), 94.44% on SZU RGB-D, 87.71% on OU-MVLP, and 86.6% on Gait3D, and compare these results against several appearance-based and model-based methods. The paper also includes an ablation study over RNN variants and a brief analysis of landmark subsets and clothing/bag effects.

Significance. If the reported accuracies were reproducible, the contribution would be noteworthy: a model-based method using only six frames of 2D landmarks would rival or exceed the state of the art on multiple benchmarks in both indoor and in-the-wild settings, with a lightweight architecture that could enable real-time gait identification. The use of Mediapipe landmarks and Procrustes alignment is a sensible design choice for view robustness. However, the significance of the paper is contingent entirely on the validity of the empirical evaluation; because the evaluation protocols are not fully specified and several reported numbers are internally inconsistent, the claimed advances cannot currently be assessed. The paper would make a stronger contribution if it provided a complete, reproducible evaluation protocol and reconciled the inconsistencies in the distance analysis.

major comments (5)
  1. [Sections 4.1 and 4.7, Tables II-V] The evaluation protocol is not defined for CASIA-B, SZU, or Gait3D. For CASIA-B, the paper only states that the first 74 subjects are used for training and the remaining for testing; it does not specify which of the six NM sequences per subject serve as gallery versus probe, how gallery and probe frames are sampled, or how the per-view accuracies at 54°, 90°, and 126° are computed. For SZU and Gait3D, no gallery/probe construction is provided at all. Additionally, the inference-time matching rule is missing: the paper does not state which model output (e.g., last hidden state, concatenated bidirectional output, or post-dense sigmoid value) is used as the embedding, nor which distance metric (Euclidean, cosine) is used for Rank-1 retrieval. Without these details, the central accuracy numbers in the abstract are unverifiable and unreproducible, so the paper's core claim does not meet standard evaluation requirements.
  2. [Section 4.4 and Figure 6] The choice of 400 training pairs is based on observed performance: the text says 'As the number of pairs increases, the model’s performance improves. Based on this observation, we chose to utilize 400 pairs for training.' Figure 6 plots 'Dataset Pair Size and Accuracy' but does not specify whether this accuracy is on a validation set or the final test set used for the reported results. If the accuracy is a test-set measurement, then the model hyperparameters are tuned on the test set, which invalidates the reported generalization performance. Even if a validation set was used, the paper must say so explicitly and describe the validation protocol. The paper also omits how positive pairs are generated per subject (e.g., how many sequences per subject, whether the same sequence is compared with itself), how negative pairs are randomly sampled, and whether the 74 positive pairs for CASIA-B correspond to one pair per training subject or cross-sequence pairs, making the training procedure non-reproducible.
  3. [Section 4.6, Table I] The Euclidean distances between encoded vectors of different individuals are implausibly small in light of the claimed 95.7% Rank-1 accuracy. For example, the distance between P1 and P3 is reported as 0.002, and the distance between P1 and P5 is 0.014. With such tiny inter-class margins, a nearest-neighbor or distance-based ranker would be expected to confuse these subjects almost always, unless the table is computed from a non-representative subset or the distance metric used for matching is different from the Euclidean distance shown here. The paper must clarify how these encoded vectors were produced, which network layer they correspond to, why such small distances still yield correct identification, and how Table I is consistent with the Rank-1 results in Table II. Without this reconciliation, the learned representation's discriminative quality is in question.
  4. [Section 4.2] The claim that six frames selected by monitoring the left foot landmark's x-coordinate 'progressing from the most negative to the most positive' constitute a complete gait cycle is not justified. In normal gait, the foot's x-coordinate oscillates with the swing and stance phases; a monotonic progression from negative to positive over six frames would typically span only a portion of a cycle (roughly a half-cycle of forward motion), not a complete cycle. The paper does not present evidence (e.g., a plot of the x-coordinate trajectory over time) or a reference that this selection yields a full cycle across viewpoints and subjects. This assumption is load-bearing because the fixed sequence length N=6 is the sole temporal input to the GRU; if the frames do not consistently align with a complete gait cycle, the model may be learning from incomplete or misaligned motion cues, which would affect the validity and transferability of the reported accuracies.
  5. [Sections 4.2 and 4.4, Table V] The reported 86.6% Rank-1 accuracy on Gait3D is well above published results on that dataset (e.g., GaitBase 64.6% as cited by the authors), yet the paper does not describe how the model and preprocessing are adapted to Gait3D. Gait3D provides 3D SMPL models and multi-view videos, while the method described in Section 4.2 is based on Mediapipe 2D landmarks extracted from video frames. The text only says that 'In the Gait3D dataset, sequences are sourced from 4,000 subjects, with 3,000 subjects used for training and 1,000 for testing,' which is insufficient to understand whether landmarks are computed from RGB frames, projected from 3D SMPL, or obtained in some other way, and how the six-frame selection criterion is applied in an in-the-wild setting with varying camera viewpoints. The absence of these details makes the Gait3D result unverifiable and suggests a possible mismatch between the described pipeline and the dataset used for evaluation.
minor comments (5)
  1. [Section 3, Equations (1)-(9)] The notation '*' is used for operations in the GRU equations but is never defined; depending on the context it could mean matrix multiplication, element-wise multiplication, or concatenation. This ambiguity makes the architectural description difficult to follow.
  2. [Section 4.5, Equation (11)] The contrastive loss is credited to reference [84] (Khosla et al., Supervised Contrastive Learning), but that reference describes a different loss formulation. The standard contrastive loss for Siamese networks typically cites Hadsell et al. (2006); the authors should cite the appropriate source and ensure the loss function is correctly referenced.
  3. [Section 4.4 and Figure 6] Figure 6's caption 'Relations Between Dataset Pair Size and Accuracy' does not specify which dataset is used, which accuracy metric is plotted, or whether the plotted points are train or test accuracies; the figure should be self-contained and clearly annotated.
  4. [Section 4.6, Table I] The table lists subjects P1, P3, P4, P5, P8, P9, P12, P13, omitting subjects P2, P6, P7, P10, and P11 without explanation; if these subjects were excluded for a reason, the authors should state why.
  5. [Throughout] The paper contains numerous typographical and grammatical errors that should be corrected, including 'avaialble' in the abstract, 'procrustus' in Section 6, and the phrase 'two bidirectional bidirectional Gated Recurrent Units' in Section 3.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported accuracies are empirical held-out results and do not reduce by construction to the model inputs or to self-citations.

full rationale

The paper's central claims are the Rank-1 accuracies reported for CASIA-B, SZU RGB-D, OU-MVLP, and Gait3D. These are empirical measurements on held-out test partitions, not quantities derived by definition from the model's inputs. The contrastive loss in Eq. 11 and the Rank-1 formula in Eq. 12 define training and evaluation objectives, but the reported percentages are not algebraically identical to any fitted parameter or preprocessing choice. The selection of N=6 frames follows an external prior work [39] and is stated as an experimental setup choice, not as a prediction; Procrustes alignment is a standard geometric preprocessing step and is not used to define the accuracy outcome. Citations to prior work by co-author Anwary et al. appear only in the related-work discussion and do not carry the derivation of the reported results. The main weaknesses are reproducibility concerns: the gallery/probe protocol is fully specified only for OU-MVLP, and the evaluation-time embedding and distance rule for Rank-1 is not stated for the other datasets. Those omissions affect verifiability and correctness risk, but they are not circularity. No equation or fitted value in the paper reduces the claimed accuracy to the model's inputs by construction, and no load-bearing argument rests on an unverified self-citation.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central accuracy claims rest on a chain of unverified domain assumptions: Mediapipe landmark quality, the six-frame gait-cycle proxy, Procrustes alignment preserving identity, and representative pair sampling. The only explicitly fitted numbers are the frame count and pair count, both chosen by observed performance.

free parameters (4)
  • Number of frames per sequence N = 6
    Chosen in Section 4.2 ('we consider six sequential frames following the experimental setup described in [39]'); affects all input representations.
  • Number of training pairs = 400 for CASIA-B and SZU
    Selected in Section 4.4 after observing Figure 6 that accuracy improves with pair count; this is a performance-based choice.
  • Positive-negative pair ratio = 1:1 for OU-MVLP, 1:2 for Gait3D
    Stated in Section 4.4 without justification; affects training dynamics.
  • Contrastive loss margin m = unspecified
    Hyperparameter in Eq. 11, never given a value in the paper.
assumptions (5)
  • domain assumption Mediapipe pose landmark estimates are accurate on all four datasets
    The whole input stream is Mediapipe landmarks (Section 4.2); no quality check or failure analysis is provided.
  • domain assumption The six-frame left-foot-extremum window covers a complete gait cycle
    Section 4.2 asserts this, but a single foot coordinate crossing from negative to positive is not a standard definition of a gait cycle.
  • domain assumption Procrustes alignment removes viewpoint differences without discarding identity cues
    Section 4.3 describes alignment but provides no quantitative verification that identity information survives.
  • domain assumption Randomly sampled negative pairs are representative
    Section 4.4 samples a subset of 74C2 negative pairs but gives no sampling scheme or repeatability analysis.
  • ad hoc to paper Contrastive loss with unspecified D is a valid training objective
    Eq. 11 uses Euclidean distance D between feature representations, but the architecture's concatenation and sigmoid output do not define D; this is an internal inconsistency.

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Cite this review

Pith. "Pith review of A Bidirectional Siamese Recurrent Neural Network for Accurate Gait Recognition Using Body Landmarks." pith.science (2026). https://pith.science/paper/LQ7PGY3D

@misc{pith2026241203498,
  author       = {Pith},
  title        = {Pith review of: A Bidirectional Siamese Recurrent Neural Network for Accurate Gait Recognition Using Body Landmarks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LQ7PGY3D}},
  note         = {Machine review of arXiv:2412.03498}
}
read the original abstract

Gait recognition is a significant biometric technique for person identification, particularly in scenarios where other physiological biometrics are impractical or ineffective. In this paper, we address the challenges associated with gait recognition and present a novel approach to improve its accuracy and reliability. The proposed method leverages advanced techniques, including sequential gait landmarks obtained through the Mediapipe pose estimation model, Procrustes analysis for alignment, and a Siamese biGRU-dualStack Neural Network architecture for capturing temporal dependencies. Extensive experiments were conducted on large-scale cross-view datasets to demonstrate the effectiveness of the approach, achieving high recognition accuracy compared to other models. The model demonstrated accuracies of 95.7%, 94.44%, 87.71%, and 86.6% on CASIA-B, SZU RGB-D, OU-MVLP, and Gait3D datasets respectively. The results highlight the potential applications of the proposed method in various practical domains, indicating its significant contribution to the field of gait recognition.

Figures

Figures reproduced from arXiv: 2412.03498 by the authors.

Figure 1
Figure 1. Access Control Based on Gait Sequence Matching: Successful vs. Unsuccessful Cases. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Proposed Framework for Gait Recognition Using Sequential Landmarks. [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Siamese BiGRU-dualStack Neural Network Architecture. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Overview of CASIA-B and SZU RGB-D Gait Datasets. [PITH_FULL_IMAGE:figures/full_fig_p015_4.png]
Figure 5
Figure 5. Figure 5: Landmark Shape Alignment Using Procrustes Analysis. [PITH_FULL_IMAGE:figures/full_fig_p018_5.png]
Figure 6
Figure 6. Figure 6: Relations Between Dataset Pair Size and Accuracy. [PITH_FULL_IMAGE:figures/full_fig_p019_6.png]
Figure 7
Figure 7. Figure 7: Visualizing Encoded Gait Variations for a Subset of Individuals. [PITH_FULL_IMAGE:figures/full_fig_p022_7.png]
Figure 8
Figure 8. Figure 8: Landmarks on Human Subjects with Loose Clothing and Carrying Bags. [PITH_FULL_IMAGE:figures/full_fig_p027_8.png]

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Reference graph

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Pith tools

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