Deep Sparse Band Selection for Hyperspectral Face Recognition

Fariborz Taherkhani; Jeremy Dawson; Nasser M. Nasrabadi

REVIEW 4 major objections 6 minor 107 references

Deep Sparse Band Selection for Hyperspectral Face Recognition

T0 review · 4 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A CNN regularized with group Lasso automatically selects a small set of spectral bands for face recognition, and on three public hyperspectral datasets this selection beats both all-band training and prior band-selection methods.

desk verdict A coherent group-Lasso band-selection method whose reported state-of-the-art advantage is not verifiable from the paper as written. read the letter →

arxiv 1908.09630 v1 pith:BBT7DDS2 submitted 2019-08-15 cs.CV

Fariborz Taherkhani , Jeremy Dawson , Nasser M. Nasrabadi This is my paper · ORCID

classification cs.CV

keywords hyperspectralfacerecognitionbandselectiongrouplassostructuredsparsityconvolutionalneuralnetworkcenterlossspectraldeeplearning

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 claims that hyperspectral face recognition is best done not with all available spectral bands but with a small automatically chosen subset, and that a convolutional network can learn that subset while it learns to recognize faces. The proposed method, Deep-SSL, feeds images from every band into a CNN and adds a group-Lasso penalty on the first-layer filter weights, which drives the weights of redundant spectral bands to zero during training. On three public hyperspectral face datasets (CMU, HK PolyU, and UWA), the resulting network selects three or four bands, mostly in the green, red, and near-infrared range, and reaches accuracy slightly above what the same network achieves with all bands. If correct, the work replaces manual or greedy band selection with a selection that is learned end-to-end as part of recognition, and it points to which wavelengths actually carry identity information.

What carries the argument

The load-bearing mechanism is group-Lasso regularization applied to the weights of the first convolutional layer. Each group consists of all first-layer weights that share the same input channel, and since each input channel is one spectral band, zeroing a group removes that band from the network. The total loss is the face-recognition loss (softmax plus center loss) plus a scaled $\ell_{1,2}$ norm of the groups; the $\ell_1$ part encourages entire groups to vanish while the $\ell_2$ part keeps weights within a surviving group intact. During training the optimizer jointly minimizes recognition error and this structured sparsity penalty, so the network itself decides which wavelengths are redundant. The center loss pulls features of the same subject together, supplying the discriminative signal that the band-selection penalty is balanced against.

What would settle it

Re-run PLS* and S-CNN+SVM* under the exact gallery/probe splits, preprocessing, and random seeds used for Deep-SSL on all three datasets. If either baseline reaches or exceeds Deep-SSL's accuracy under identical conditions, the paper's central state-of-the-art claim fails; if Deep-SSL still wins, the claim stands even under scrutiny.

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Extended reading notes

Core claim

The central claim is that Deep-SSL, a CNN whose first convolutional layer is regularized by a group-Lasso term, achieves the highest face recognition accuracy reported on the CMU, HK PolyU, and UWA hyperspectral face datasets, outperforming prior methods including PLS-based regression and a CNN with AdaBoost band selection. The same network without the sparsity term already scores in the high 99% range, and adding the group-Lasso penalty raises accuracy by roughly 0.3 to 0.6 percentage points while shrinking the input from 33 or 65 bands to 3 or 4 bands. The selected bands cluster in the green, red, and infrared portions of the spectrum, and the blue bands are zeroed out first. The paper presents this as evidence that jointly optimizing band selection and face recognition, rather than selecting bands greedily or manually, yields both higher accuracy and a smaller, cheaper spectral acquisition requirement.

Load-bearing premise

The claim that Deep-SSL beats prior methods rests on the assumption that the accuracies reported for PLS*, S-CNN+SVM*, and other baselines were measured under exactly the same gallery/probe splits, preprocessing, and evaluation protocol as Deep-SSL; the paper states that the setup was consistent but does not re-run the baselines or publish the splits and seeds.

Editorial extensions

If this is right

If Deep-SSL's results hold, hyperspectral face recognition systems can drop the majority of their captured bands and still match or slightly exceed all-band accuracy, which simplifies sensor design and reduces data volume.
The selected bands from all three datasets fall in the green, red, and near-infrared range and exclude blue, suggesting those wavelengths carry the most identity information for faces under indoor illumination.
Because the sparsity penalty acts on the first layer of any CNN, the same training scheme can be transplanted to other deep backbones and other hyperspectral tasks without changing the network's task-specific layers.
The comparison table places Deep-SSL above PLS-based regression and CNN-plus-AdaBoost band selection on all three datasets, making it the current best reported method for hyperspectral face recognition when all methods use their optimal bands.

Reading between the lines

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

Beyond the paper, the same group-Lasso channel-selection trick could be applied to hyperspectral remote sensing or medical imaging, where the selected wavelengths would be task-specific rather than tied to faces.
A testable extension is to verify whether the selected band set is stable across illumination conditions, skin types, and sensor hardware; the paper's three datasets are all indoor, and outdoor or cross-session data could shift the optimal wavelengths.
The accuracy gains over using all bands are small, so the practical value may lie less in raw accuracy and more in the ability to build cheaper multi-spectral cameras that capture only the selected bands.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Two things to know. The core mechanism is straightforward: the authors put a group-Lasso penalty on the first convolutional layer of a VGG-19, with each spectral band as a group, and zero out redundant bands during training. That is a clean, sensible application of known structured-sparsity ideas, and the loss in (3)-(6) is unambiguous. The paper also gives per-band accuracy plots, which help show why the selected bands make sense.

The soft spot is the evidence for the headline claim. Table 4 compares Deep-SSL against a long list of prior methods, but those numbers appear to be taken from earlier papers rather than re-run under the same splits and preprocessing. The authors assert consistency in setup, but give no split definitions, no seeds, and no code or data at submission. On top of that, the paper has internal arithmetic problems: the PolyU probe count in Section 9.9 is 63, but 113 cubes minus 48 gallery cubes leaves 65; Section 9.4 says 300 PolyU cubes while Table 1 uses 113; and Section 9.7 says UWA selection found three bands, while Table 2 lists four. These are the sort of thing that makes a careful reader stop trusting the reported numbers.

The gains over the all-band baseline are also tiny: 0.32 to 0.59 percentage points at 99.9% accuracy. Without error bars or multiple random splits, that margin is plausibly one or two probe images, so the "outperforms all bands" claim could easily be split noise. The mechanism could still work; the paper just does not demonstrate it convincingly.

Who is this for? Researchers working on hyperspectral face recognition or band selection. It deserves a serious referee because the method is coherent and the datasets are public in principle, but the current write-up needs real changes: re-run or clearly identify the baseline numbers, provide error bars and split info, fix the inconsistencies, and release code/data. If those are addressed, it could become a solid niche contribution. As it stands, the stated SOTA advantage is unverified.

Referee Report

4 major / 6 minor

Summary. The manuscript proposes Deep-SSL, a VGG-19-based CNN for hyperspectral face recognition with automatic spectral-band selection. The first convolutional layer has one input channel per spectral band, and a group-Lasso (structural sparsity learning) penalty Rg(w1) in Eqs. (5)-(6) is added to the softmax-plus-center loss in Eqs. (3)-(4), so that groups of first-layer weights corresponding to redundant bands are driven to zero during joint training; band selection is therefore end-to-end rather than manual or greedy. The method is evaluated on three public datasets (CMU-HSFD, HK PolyU-HSFD, UWA-HSFD) with reported accuracies of 99.9%, 99.8%, and 99.9%, respectively. The central assertion (Section 9.9, Table 4) is that Deep-SSL outperforms state-of-the-art methods including PLS* and S-CNN+SVM*, and that band selection improves accuracy over the all-band deep baseline by 0.32-0.59 percentage points (Section 9.8, Table 3).

Significance. If the comparison were controlled, this would be a useful contribution: Eqs. (3)-(6) give a clear group-Lasso formulation for channel-level band selection trained jointly with recognition, avoiding manual and greedy selection, and the evaluation covers three public datasets with a broad slate of comparison methods in Table 4. Credit is due for the clean formulation and the multi-dataset effort. However, the marginal gain of the band-selection mechanism over the all-band deep baseline is only 0.32-0.59 percentage points at near-ceiling accuracy (99.3-99.9%), so the substantive advantage claimed for band selection is small and not yet shown to be statistically stable; the dominant effect is the strength of the deep baseline itself. The headline state-of-the-art claim is not robustly evidenced, and several internal inconsistencies weaken the experimental record. The core idea is plausible and publishable, but the evidence needs substantial strengthening.

major comments (4)

[Section 9.9, Table 4] The headline assertion that Deep-SSL outperforms PLS* and S-CNN+SVM* is based on accuracies taken from the original papers rather than on re-runs under the protocol used for Deep-SSL. The sentence 'we have been consistent with other compared methods in experimental setup' asserts protocol matching, but no split definitions, random seeds, preprocessing details (e.g., alignment, cropping, image resolution), or per-split results are given for the prior methods, and code is not released. Because the prior methods use entirely different feature pipelines (e.g., 3D-DCT and PLS in [3]) and possibly different gallery/probe compositions, the numbers in Table 4 cannot be verified as comparable; if the prior-method results come from different splits, the stated state-of-the-art advantage fails even if the group-Lasso mechanism itself works. The authors should either re-run all baselines under the single evaluation protocol used for Deep-SSL and report per-split and mean accuracy, or explicitly downgrade the claim to 'competitive with previously published results.'
[Sections 9.4, 9.7, 9.9; Tables 1 and 2] The experimental description is internally inconsistent in several places. Section 9.4 states that the HK PolyU dataset contains 300 hyperspectral cubes from 24 subjects, while Table 1 and Section 9.9 state 113 cubes. Section 9.9 says the PolyU gallery uses two cubes per subject (48 cubes) with 'the remaining 63 cubes' as probes, but 113 - 48 = 65. Section 9.7 reports three selected UWA bands {570, 650, 680}, while Table 2 lists four UWA bands {570, 650, 680, 710}. These discrepancies are not merely typographical: the probe-count error changes the interpretation of the claimed 0.36% improvement (a fraction of one probe image), and the band-list conflict makes the selected-band result ambiguous. All of these numbers must be reconciled before the evaluation can be assessed.
[Section 9.8, Tables 3 and 4] No error bars or multiple-run statistics are reported, although gallery images are chosen randomly (Section 9.9) and lambda_g is selected by cross-validation (Section 9.3). With probe sets of approximately 99 (CMU), 63 or 65 (PolyU), and 50 (UWA) images, the reported SSL gains over the all-band baseline (0.59%, 0.36%, and 0.32%, respectively) correspond to less than one probe image per dataset, so the improvement attributed to band selection is within plausible split-to-split noise unless demonstrated otherwise. In addition, Table 4 reports Deep-SSL PolyU accuracy as 99.8%, while Table 3 reports 99.88%, which rounds to 99.9%; the discrepancy should be resolved. Report mean and standard deviation over multiple random splits and per-split accuracies for Deep-Baseline and Deep-SSL.
[Sections 8.4-8.5, 9.2, 9.7] The mapping from the trained weights to the selected-band lists in Table 2 is not specified. Since the network is trained with Adam rather than a proximal-gradient method, the group norms likely only approach zero in practice; the paper does not state the threshold used to declare a band zeroed out, nor whether the network is retrained after the claimed depth reduction C' << C (Section 8.5), and the specific lambda_g values used per dataset are not reported (Fig. 8 gives only a sensitivity range). The initialization of the first-layer filters by duplicating the ImageNet RGB filters (Section 9.2) is an unvalidated heuristic that could bias which input channels survive group sparsification. An ablation with random initialization and a precise description of how the band lists are extracted from the trained filters are needed for the band-selection result to be reproducible.

minor comments (6)

[Section 9.4 vs. Table 1] The CMU spectral range is stated as 450-1100 nm in the text but 450-1090 nm in Table 1; since 65 bands at 10-nm steps implies 450-1090 nm, the text should be corrected.
[Section 9.8 vs. Figs. 9-10] The text assigns Fig. 9 to CMU and Fig. 10 to PolyU, while the figure captions assign Fig. 9 to HK PolyU-HSFD and Fig. 10 to CMU-HSFD; the assignments should be made consistent.
[Section 2.2] An unfinished citation placeholder '[?,?,?,?]' appears in the deep-learning face-recognition sentence and must be completed.
[References; Eqs. (1) and (3)] References [41] and [86] both cite Simonyan and Zisserman (VGG) and should be merged; the notation L(w) is used for two different losses in Eqs. (1) and (3) and should be disambiguated.
[Section 8.4, Eq. (5)] The notation 'l_12 norm' for the group penalty in Eq. (5) is non-standard; since Eq. (6) is an l1 sum of l2 norms, the standard notation is l2,1 (or group Lasso), and this should be stated explicitly.
[Section 9.9] The stray footnote marker in 'Please email us 1' should be removed, and the offer of code and data only by email limits reproducibility; public release of the code and split definitions is recommended.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the band-selection pipeline is a trained CNN output and accuracies are measured on held-out probes; protocol inconsistencies concern validity, not circular reasoning.

full rationale

The paper's core derivation is self-contained. Equations (3)-(6) define a training objective that combines a face-recognition loss with a group-Lasso penalty on the first convolutional layer, whose channel groups correspond to spectral bands. The selected bands are the sparsity pattern induced by training, and the reported accuracies in Tables 3 and 4 are obtained on gallery/probe splits that are separate from the training process that produces the sparsity pattern. No equation defines the selected bands in terms of the final accuracies, and no reported accuracy is a fitted parameter renamed as a prediction. Tuning the hyperparameter lambda_g by cross-validation is a standard model-selection procedure, not a circular step. The paper contains many self-citations, mostly in the related-work survey, but none is load-bearing: the group-Lasso mechanism is cited to Yuan and Lin [54], the center loss to Wen et al. [95], the base network to Simonyan and Zisserman, and the main baselines to Uzair et al. [3] and Sharma et al. [51], none of whom are the present authors. There are genuine validity concerns outside circularity: Table 4 imports prior-method accuracies without re-running them, no split seeds are provided, and the dataset counts are internally inconsistent (PolyU reported as both 300 and 113 cubes; Section 9.9 says 63 remaining probes when 113-48=65; UWA selected bands are listed as 3 in the text but 4 in Table 2). These issues undermine the reliability of the state-of-the-art comparison, but they do not exhibit the specific reduction of a prediction to its own input that circularity requires. Therefore no circular step is established.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests on group Lasso's group-zeroing property, which is standard, on the channel-to-spectral-band correspondence, on a duplicated pretrained filter initialization, and on comparability of prior-method accuracy numbers. The sparsity weight lambda_g and the center loss weight gamma are free hyperparameters whose final values are not reported for the headline results.

free parameters (3)

lambda_g (sparsity weight) = not reported; cross-validated over {10, 1, 0.1, 0.01, 0.001}
Balances face recognition loss and group Lasso penalty in Eq. (3). Section 9.5 sweeps values but does not state the value used for Tables 3 and 4.
gamma (center loss weight) = not reported
Center loss weight in Eq. (4). No value or tuning procedure is given.
center update learning rate = 0.001
Fixed update rate for class centers in Section 9.6, following Wen et al. [95]; set by hand.

assumptions (5)

standard math Group Lasso can zero out entire groups of weights during optimization.
Relied on in Section 8.4 to remove whole spectral bands; standard result from Yuan and Lin [54].
domain assumption First-layer weight groups indexed by channel correspond one-to-one with spectral bands.
Section 8.4 defines groups as w1(:,c,:,:) and assumes each channel is a co-registered band, so zeroing a group discards that band.
ad hoc to paper Duplicating pretrained RGB VGG-19 filters is a valid initialization for hyperspectral depth.
Section 9.2 initializes first-layer filters by duplicating ImageNet RGB filters; no evidence shows this does not bias which bands are selected.
domain assumption Prior-method accuracies in Table 4 are comparable under the same experimental setup.
Section 9.9 claims consistency, but prior methods are not re-run and no split definitions or seeds are provided.
domain assumption Hyperspectral cubes are co-registered across bands and can be fed directly as channels.
The method assumes no band misalignment and no per-band preprocessing beyond mean subtraction; Section 9.1.

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Cite this review

Pith. "Pith review of Deep Sparse Band Selection for Hyperspectral Face Recognition." pith.science (2026). https://pith.science/paper/BBT7DDS2

@misc{pith2026190809630,
  author       = {Pith},
  title        = {Pith review of: Deep Sparse Band Selection for Hyperspectral Face Recognition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BBT7DDS2}},
  note         = {Machine review of arXiv:1908.09630}
}

read the original abstract

Hyperspectral imaging systems collect and process information from specific wavelengths across the electromagnetic spectrum. The fusion of multi-spectral bands in the visible spectrum has been exploited to improve face recognition performance over all the conventional broad band face images. In this book chapter, we propose a new Convolutional Neural Network (CNN) framework which adopts a structural sparsity learning technique to select the optimal spectral bands to obtain the best face recognition performance over all of the spectral bands. Specifically, in this method, images from all bands are fed to a CNN, and the convolutional filters in the first layer of the CNN are then regularized by employing a group Lasso algorithm to zero out the redundant bands during the training of the network. Contrary to other methods which usually select the useful bands manually or in a greedy fashion, our method selects the optimal spectral bands automatically to achieve the best face recognition performance over all spectral bands. Moreover, experimental results demonstrate that our method outperforms state of the art band selection methods for face recognition on several publicly-available hyperspectral face image datasets.

Figures

Figures reproduced from arXiv: 1908.09630 by the authors.

**Figure 1.** Building the spectral data cube in both line scan and snapshot systems In contrast to scanning methods, a snapshot hyperspectral camera can capture hyperspectral image data in which all wavelengths are captured instantly to create the hypercube, as shown in [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

**Figure 2.** Convolution operation [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

**Figure 3.** Max pooling operation. spatially arranged so there cannot be a convolution layer after a fully connected layer. Currently, some deep architecture have their FC layer replaced, as in NIN, where FC layer is replaced by a GAP layer. 7.4 Classification Layer The last FC layer serves as the classification layer that calculates the loss or error which is a penalty for discrepancy between actual output and desired. For pre… view at source ↗

Figures from the paper (9 more)

**Figure 4.** Figure 4: ReLu activation function. 7.5 Activation Function: ReLU ReLU is the regular activation function that is used in CNN models. It is a linear activation function which has thresholding at zero as shown in Eq.2. It has been shown that the convergence of gradient descent i…

**Figure 5.** Figure 5: Block diagram of hyperspectral band selection for face recognition based on structurally sparsified CNN. 8 SSL Framework for Band Selection We propose a regularization scheme which uses a SSL technique to specify the optimal spectral bands to obtain the best face reco…

**Figure 6.** Figure 6: Face recognition accuracy of each individual band on the UWA -HSFD 9 Experimental Setup and Results 9.1 CNN Architecture We use the VGG-19 [86] architecture as shown in [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]

**Figure 7.** Figure 7: Samples of hyperspectral images. Dataset Subjects HS Cubes Bands Spectral Range CMU 48 147 65 450-1090 nm HK PolyU 24 113 33 400-720 nm UWA 70 120 33 400-720 nm [PITH_FULL_IMAGE:figures/full_fig_p019_7.png]

**Figure 8.** Figure 8: Accuracy of our model using different values of λg [PITH_FULL_IMAGE:figures/full_fig_p020_8.png]

**Figure 9.** Figure 9: Face recognition accuracy of each individual band on the HK PolyU-HSFD different values of {10, 1, 10−1 , 10−2 , 10−3}, respectively. We can see that our network total loss defined in (3) is not significantly sensitive to λg if we set these parameters within [10−3 , 10…

**Figure 10.** Figure 10: Face recognition accuracy of each individual band on the CMU-HSFD all the spectral bands but more than three bands in RGB for the CMU dataset. Specifically, the results demonstrate that the most discriminative spectral wavelengths for face recognition are obtained by…

**Figure 11.** Figure 11: Images of selected bands from UWA dataset. selected by our method. Our algorithm selects 4 bands including {750, 810 , 920, 990} for the CMU dataset, 3 bands including {580, 640, 700} for PolyU, and 3 bands including {570, 650, 680} for the UWA dataset. The results s…

**Figure 12.** Figure 12: Images of selected bands from CMU dataset. Dataset Bands CMU {750, 810, 920, 990 }nm HK PolyU {580, 640, 700 }nm UWA { 570, 650, 680,710}nm [PITH_FULL_IMAGE:figures/full_fig_p023_12.png]

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