Paper · 2021-02-18 · cs.CV

Deep Gait Recognition: A Survey

Alireza Sepas-Moghaddam, Ali Etemad

Survey of vision-based gait recognition with deep learning, covering work up to January 2021. It proposes a four-dimension taxonomy (body representation, temporal representation, feature representation, neural architecture), reviews 15 gait datasets and their test protocols, and tabulates the reported results. Best author-reported CASIA-B average: 90.4% (3DCNNGait).

Convolutional neural network Recurrent neural network (RNN)Generative adversarial network (GAN)Metric learningsurveygait-recognitionbiometricscomputer-vision

arXiv PDF Markdown18 min read

AI-compiled by claude-haiku-5-5 on 2026-11-06, not yet reviewed by a human. Every claim cites its source; check it when a detail matters.

In plain words

Gait recognition means identifying a person from the way they walk, using video of them taken from some distance. This paper is a survey: it reads the research on that problem since deep neural networks became common, and organizes it.

The authors sort the work along four questions. How is the body shown to the network: as a silhouette outline or as a set of joint positions? How is time handled: by squashing a whole walk into one summary picture, or by keeping the frames in order? Which part of the body is looked at: the whole figure or separate regions? And which network design is used?

The paper also lists the public video datasets people train and test on, explains how the tests are usually run, and collects the reported accuracy numbers in tables. It ends with open problems, such as disentangling identity from clothing and carried objects, learning without labels, and testing on datasets the model was not trained on.

TL;DR

A survey of deep gait recognition from video up to January 2021, with a four-dimension taxonomy of methods, 15 datasets with their protocols, and comparison tables for CASIA-B, OU-ISIR and OU-MVLP. The authors report that silhouettes are the dominant body representation and CASIA-B is the most used dataset. Use it to find which methods, datasets and test protocols exist. Do not use its numbers as a fair ranking: they are copied from the original papers, and the paper's own sources of error are listed under Pitfalls.

Problem

The paper lists what degrades gait recognition: clothing and carried objects, viewpoint, self-occlusion, complex backgrounds and lighting §1. The person's own state (fatigue, excitement or fear, injury) can also change the walk §1.1. Section 1.1 also explains why gait differs from face or fingerprint recognition: it works at a distance and without the subject's cooperation, and it is hard to fake. Gait is also a finer signal than the activity-recognition problems it is often compared with, because walking is the common base and the identity cues are small differences within it §1.1.

Before this survey, the vision-based gait reviews stopped at about mid-2018 and none focused on deep methods §1.2. The paper states that the field changed sharply after 2015 and that most methods from 2019 and 2020 use deep networks §1.2 fig. 1.

Method

This is a literature review. There is no model, loss or training procedure to implement. The "method" is the search and classification procedure, and the reference data that goes with it. An implementer should use the tables and protocols, not the survey text, as the source.

Search protocol §2. Candidate papers came from Google Scholar, IEEE Xplore, the ACM Digital Library, ScienceDirect and CVF Open Access. Search terms: "gait recognition", "gait identification", "gait biometric", "neural architecture", "deep learning", "deep representations". Forward and backward citation chasing followed. The date filter starts in 2014.
Inclusion rules §2. Papers were excluded if they (i) use non-vision sensors, (ii) propose no new solution, (iii) use non-standard or private datasets, or (iv) do not compare with the state of the art. Where a paper combines vision with another modality, only the vision part is reviewed.
Test protocols §3.1 fig. 3. Protocols are subject-dependent (training and test sets share subjects, as in TUM GAID) or subject-independent (test subjects are disjoint from training subjects, as in CASIA-B and OU-MVLP). In the subject-independent case, the test subjects are split into gallery and probe sets. A classifier then matches each probe against the gallery. All results are reported as rank-1 accuracy §3.1.
Datasets §3.2 tab. 1. Fifteen vision datasets are reviewed. Table 1 gives the data type, subjects and sequences, environment, number of views and the variations covered. Dataset-specific protocols are described in §3.2 and their parameters are in the Hyperparameters section below.
Body representation §4.1. Two options: silhouettes (binary foreground masks) or skeletons (joint positions from a depth camera or a pose estimator). Silhouettes are cheap and focus on gait rather than clothing, but they are sensitive to appearance. Skeletons are more robust to viewpoint and clothing, but they depend on accurate joint detection and need extra computation §4.1.
Temporal representation §4.2 fig. 5. Two families. Templates collapse a sequence into one map, for example by averaging silhouettes over one gait cycle or over the whole sequence. This makes the output independent of sequence length. Volumes keep the frame order and feed a 3D tensor to a 3D CNN or a recurrent network §4.2. Template variants named in §4.2: gait energy image (GEI), chrono gait image (CGI), frame-difference energy image (FDEI), gait entropy image (GEnI), period energy image (PEI); convolutional templates such as set pooling and GCEM.
Pseudocode
```
# Gait energy image (GEI)  [§4.2]
# Input:  silhouettes (T, H, W), binary
# Output: template (H, W)
def gait_energy_image(silhouettes):
    # Average the silhouettes over one gait cycle or the whole sequence  [§4.2]
    template = mean(silhouettes, axis=0)     # (H, W)
    return template
```
Feature representation §4.3. Global methods learn from the whole silhouette or skeleton. Partial methods split it into regions (patches, body parts, horizontal bins) and learn each region, often with RNNs, capsules, attention or fully connected layers §4.3. Partial methods can learn how much each region contributes §4.3.
Neural architectures §4.4. Nine families, each with its own subsection:
- CNN §4.4.1 tab. 2. The most used family. Most networks have fewer than 10 layers, and the input is 64×64 in most recent work. The paper cites an analysis (ref. B6) finding that 64×64 and 128×128 give the best results, with 128×128 slightly better §4.4.1.
- DBN §4.4.2. Two methods use stacked restricted Boltzmann machines (refs. Biom and DBN2).
- RNN §4.4.3. LSTM and GRU are the common cells. Three uses: RNNs on joint trajectories (ref. LSTM3), RNN combined with CNN (refs. LSTM1, LSTM2), and RNN over partial features of a convolutional template (refs. TBiom, ICPR).
- DAE §4.4.4. Encoder-decoder networks whose bottleneck features are used for recognition, with the decoder reconstructing a temporal template. Some variants split identity features from covariate features (ref. CVPR20202).
- GAN §4.4.5. Used for view transformation, clothing change or removal of carried objects, so that identity can be matched across conditions. Two discriminators are common: one for realism and one for identity preservation (ref. B9).
- CapsNet §4.4.6. Capsules with dynamic routing model relations between body parts and between hierarchical sub-parts (refs. capsule1, ICPR, spyder).
- 3D CNN §4.4.7. Spatio-temporal convolution over the stacked frames. Its stated limit is handling variable-length sequences; ref. MM uses several 3D CNNs at different temporal scales.
- GCN §4.4.8. A spatio-temporal graph built from the sequence, with a joint relation learning step (ref. GCN2).
- Hybrid §4.4.9. The four common pairings are CNN+RNN, DAE+GAN, DAE+RNN and RNN+CapsNet fig. 6 §4.4.9.
Taxonomy classification §5 tab. 3. Every reviewed method is placed on the four dimensions, with its loss function and the datasets it reports. Table 3 is reproduced in full below. The "Key" is the paper's BibTeX key from raw/src/2102.09546/references.bib. The rendered paper numbers its references, so the key is the only stable handle for a method here.

Datasets (reproduced from tab. 1 §3.2)

Dataset	Year	Data type	Subjects / sequences	Environment	Views	Variations
CMU MoBo	2001	RGB; silhouette	25 / 600	Indoor	6	3 walking speeds; carrying a ball
SOTON	2002	RGB; silhouette	115 / 2,128	Indoor and outdoor	2	Normal walking on a treadmill
CASIA-A	2003	RGB	20 / 240	Outdoor	3	Normal walking
USF HumanID	2005	RGB	122 / 1,870	Outdoor	2	Outdoor walking; carrying a briefcase; time interval
CASIA-B	2006	RGB; silhouette	124 / 13,680	Indoor	11	Normal walking; carrying a bag; wearing a coat
CASIA-C	2006	Infrared; silhouette	153 / 1,530	Outdoor	1	3 walking speeds; carrying a bag
OU-ISIR Speed	2010	Silhouette	34 / 306	Indoor	4	Nine walking speeds
OU-ISIR Clothing	2010	Silhouette	68 / 2,746	Indoor	4	Up to 32 clothing combinations
OU-ISIR MV	2010	Silhouette	168 / 4,200	Indoor	25	24 azimuth views and 1 top view
OU-ISIR	2012	Silhouette	4,007 / 31,368	Outdoor	4	Normal walking
TUM GAID	2012	RGB; depth; audio	305 / 3,737	Indoor	1	Normal walking; backpack; shoe covers
OU-ISIR LP Bag	2017	Silhouette	62,528 / 187,584	Indoor	1	Seven carried objects
OU-MVLP	2018	Silhouette	10,307 / 259,013	Indoor	14	Normal walking
CASIA-E	2020	Silhouette	1,014 / undisclosed	Indoor and outdoor	15	3 scenes; normal walk; carrying a bag; wearing a coat
OU-MVLP Pose	2020	Skeleton	10,307 / 259,013	Indoor	14	Normal walking

Source: tab. 1 §3.2. The table is sorted by release year, as in the paper.

Taxonomy classification (tab. 3) §5

Source: tab. 3 §5. Columns follow the paper. "Key" is the BibTeX key from raw/src/2102.09546/references.bib. "not stated" means the table leaves the loss blank or lists it as undisclosed. The Key capsule2 appears twice, once for 2019 (JVCI) and once for 2020 (ITNEC), as in the source.

Key	Year	Venue	Body	Temporal	Feature	Architecture	Loss	Dataset
TMM	2015	T-MM	Silhouette	GEI	Global	CNN	Cross-entropy	CASIA-B
SOTA1	2015	CISP	Silhouette	GEI	Global	CNN	not stated	CASIA-B
SOTA2	2016	ICPR	Skeleton	Sequence volume	Partial	LSTM	not stated	CASIA-B
B8	2016	ICB	Silhouette	GEI	Global	CNN	Cross-entropy	OU-ISIR
3DCNNICIP	2016	ICIP	Silhouette	Sequence volume	Global	3D CNN	not stated	CMU MoBo; USF HumanID
SOTA3	2016	ICASSP	Silhouette	GEI	Global	CNN	Contrastive	OU-ISIR
LSTM3	2016	BMVC	Skeleton	Sequence volume	Global	CNN + LSTM	Cross-entropy	CASIA-B; CASIA-A
Biom	2017	Int. J. Biom.	Silhouette	GEI	Partial	DBN	not stated	CASIA-B
SOTA4	2017	CVIU	Silhouette	GEI	Global	CNN	not stated	CASIA-B
B6	2017	IEEE T-PAMI	Silhouette	GEI	Global	CNN	Cross-entropy	CASIA-B; OU-ISIR
SOTA5	2017	Applied Sci.	Silhouette	Norm. AC	Global	CNN	not stated	OU-ISIR
SOTA6	2017	IEEE T-CSVT	Silhouette	GEI	Global	CNN	Triplet	OU-ISIR
SOTA7	2017	BIOSIG	Silhouette	GEI	Global	CNN	not stated	TUM-GAID
SOTA31	2017	MM	Silhouette	GEI	Global	CNN	Triplet	OU-ISIR
SOTA30	2017	CCBR	Skeleton	Sequence volume	Global	CNN + LSTM	not stated	CASIA-B
GAN5	2017	CVPRW	Silhouette	GEI	Global	GAN	Cross-entropy	CASIA-B
DAE1	2017	Neurocomputing	Silhouette	GEI	Global	DAE	Euclidean	CASIA-B; SZU RGB-D
SOTA22	2018	Electronic Imaging	Silhouette	Sequence volume	Global	3D CNN	not stated	CASIA-B
CNNLSTM1	2018	IEEE Access	Silhouette	Sequence volume	Global	CNN + LSTM	Cross-entropy	CASIA-C
SOTA21	2018	Neuroinformatics	Silhouette	Sequence volume + GEI	Global	3D CNN	Contrastive	OU-ISIR
DIC	2018	DIC	Skeleton	GEI	Global	CNN	Cross-entropy	CASIA-B
SOTA8	2018	IEEE Access	Silhouette	Sequence volume	Global	CNN + LSTM	Cross-entropy	CASIA-B; OU-ISIR
SOTA9	2018	ISBA	Silhouette	Sequence volume	Global	3D CNN	not stated	CASIA-B
DAEGAN2	2018	ICME	Silhouette	GEI	Part; global	DAE + GAN	Cross-entropy	CASIA-B
SOTA17	2018	JVCIR	Silhouette	GEI	Global	CNN	Cross-entropy	CASIA-B; OU-ISIR
SOTA18	2018	CCBR	Skeleton	Sequence volume	Global	CNN + LSTM	not stated	CASIA-B
PRL	2019	PRL	Skeleton; silhouette	Sequence volume	Global	LSTM	not stated	CASIA-B; TUM-GAID
IETBiom	2019	IET Biom.	Silhouette	Weighted average	Partial	CNN	not stated	CASIA-B; TUM; OU-ISIR
B3	2019	CVPR	Skeleton; silhouette	Sequence volume	Global	DAE + LSTM	Multiple loss functions	CASIA-B; FVG
SOTA27	2019	J. Sys. Arch.	Silhouette	GEI	Global	DAE + GAN	Multiple loss functions	CASIA-B; OU-ISIR
B10	2019	PR	Silhouette	GEI	Global	CNN	Siamese	CASIA-B; SZU
B7	2019	IEEE T-IFS	Silhouette	GEI	Global	GAN	Adversarial & cross-entropy	CASIA-B; OU-ISIR
SOTA24	2019	PRL	Silhouette	GEI	Global	CNN	Restrictive triplet	CASIA-B; OU-ISIR
SOTA10	2019	CVPR	Silhouette	GEI	Global	CNN	Quintuplet	CASIA-B; OU-ISIR LP Bag
GAN3	2019	Neurocomputing	Silhouette	GEI	Global	GAN	Pixel-wise and entropy	CASIA-B; OU-ISIR
SOTA26	2019	IJCNN	Silhouette	GEI	Global	GAN	Multiple loss functions	CASIA-B
R6	2019	IEEE T-IFS	Skeleton	GEI	Global	DAE	Contrastive & triplet	OU-ISIR LP Bag; TUM-GAID
opt1	2019	ICVIP	Silhouette	Weighted average	Partial	CNN	View & cross-entropy	CASIA-B
SOTA25	2019	IEEE T-MM	Silhouette	Sequence volume	Global	CNN + LSTM	Contrastive	CASIA-B; OU-ISIR
SOTA29	2019	IJCNN	Silhouette	GEI	Global	GAN	Multiple loss functions	CASIA-B
SOTA28	2019	NCAA	Silhouette	GEI	Global	CNN	not stated	CASIA-B; CASIA-A; OU-ISIR
SOTA23	2019	PR	Silhouette	Set pooling	Global	CNN	Center & softmax	CASIA-B
NCAA	2019	NCAA	Silhouette	GEI	Global	CNN	not stated	CASIA-B; OU-ISIR
capsule2	2019	JVCI	Silhouette	GEI	Global	CapsNet	Standard capsule loss	CASIA-B
B1	2019	AAAI	Silhouette	Set pooling	Partial	CNN	Batch-all triplet	CASIA-B; OU-MVLP
RNNAE	2020	IEEE Access	Skeleton	Sequence volume	Global	DAE + LSTM	Mean squared error	Walking gait (dataset not named)
PR5	2020	PR	Skeleton	Sequence volume	Partial	CNN	Center & softmax	CASIA-B; CASIA-E
B4	2020	IEEE T-PAMI	Skeleton; silhouette	Sequence volume	Global	DAE + LSTM	Multiple loss functions	CASIA-B; FVG
TIP	2020	IEEE T-IP	Silhouette	Sequence volume	Partial	CNN + LSTM	Angle center	CASIA-B; OU-MVLP; OU-LP
GAN4	2020	PR	Silhouette	GEI	Global	GAN	Multiple loss functions	OULP-BAG; OU-ISIR LP Bag
SOTA11	2020	MTAP	Silhouette	MF-GEI	Global	CNN	not stated	CASIA-B
spyder	2020	KBS	Silhouette	Sequence volume	Global	LSTM + capsule	Capsule & memory	CASIA-B; OU-MVLP
SOTA16	2020	JINS	Silhouette	GEI	Global	CNN + LSTM	not stated	CASIA-B; OU-ISIR
CSVT	2020	IEEE T-CSVT	Silhouette	GEI	Global	CNN	Contrastive & triplet	CASIA-B; OU-MVLP; OU-ISIR
arXiv	2020	arXiv	Silhouette	Sequence volume	Global	3D CNN	Triplet	CASIA-B; OU-MVLP
MTAP2	2020	MTAP	Silhouette	GEI	Partial	CNN	not stated	CASIA-B; OU-ISIR
GCN2	2020	arXiv	Skeleton	Sequence volume	Global	GCN	Triplet & ArcFace	CASIA-B
MTAP3	2020	MTAP	Silhouette	GEI	Global	CNN	not stated	CASIA-B; OU-ISIR
SOTA15	2020	JIPS	Silhouette	GEI	Global	CNN	Soft-max	CASIA-B; OU-ISIR
MTAP4	2020	MTAP	Silhouette	GEI	Global	CNN	not stated	CASIA-B
Supercom	2020	J. Supercomput.	Silhouette	GEI	Global	CNN	not stated	CASIA-B; OU-ISIR; OU-MVLP
capsule2	2020	ITNEC	Silhouette	GEI	Global	CapsNet	Standard capsule loss	CASIA-B; OU-ISIR
CVPR20201	2020	CVPR	Silhouette	Horizontal pooling	Partial	CNN	Batch-all triplet	CASIA-B; OU-MVLP
CVPR20202	2020	CVPR	Silhouette	GEI	Global	DAE	Contrastive & triplet	CASIA-B; OU-ISIR LP Bag
Tbiom2	2020	IEEE T-Biom.	Skeleton	Sequence volume	Global	CNN + LSTM	Cross-entropy & center	OUMVLP-Pose
ACCVW1	2020	ACCVW	Silhouette	Set pooling	Partial	CNN	Batch-all triplet	CASIA-E
ACCVW2	2020	ACCVW	Silhouette	Set pooling	Partial	CNN	Batch-all triplet	CASIA-E
ICPR	2020	ICPR	Silhouette	Set pooling	Partial	CNN + GRU + capsule	Triplet & cosine proximity	CASIA-B; OU-MVLP
TBiom	2020	IEEE T-Biom.	Silhouette	GCEM	Partial	CNN + GRU	Triplet & cross-entropy	CASIA-B; OU-MVLP
SOTA20	2020	IEEE Access	Silhouette	Set pooling	Global	CNN	Triplet	CASIA-B
ICASSP3	2019	ICASSP	Silhouette	Pooling	Global	CNN	Center-ranked	CASIA-B; OU-MVLP
SOTA19	2020	IJCB	Silhouette	GEI	Global	DAE + GAN	Center & soft-max	CASIA-B; OU-ISIR
ACCV	2020	ACCV	Skeleton; silhouette	Sequence volume	Global	CNN + LSTM	Multiple loss functions	CASIA-B; OU-MVLP
MM	2020	MM	Silhouette	Sequence volume	Global	3D CNN	Multiple triplet losses	CASIA-B; OU-ISIR
ECCV	2020	ECCV	Silhouette	Set pooling	Global	CNN	Triplet & cross-entropy	CASIA-B; OU-MVLP

Hyperparameters

The survey trains nothing, so it reports no training hyperparameters (no learning rates, optimizers, batch sizes or epochs). The parameters that do matter for reproducing the reported comparisons are the dataset protocol splits and the architecture facts below.

Protocol parameters

Dataset	Parameter	Value	Source
CASIA-B	Training subjects	first 74	§3.2
CASIA-B	Test subjects	remaining 50	§3.2
CASIA-B	Gallery	first 4 NM sequences per subject per view	§3.2
CASIA-B	Probe	remaining 2 NM, 2 CL and 2 BG sequences per subject per view	§3.2
CASIA-B	Probe views equal to the gallery view	excluded from the reported results	§3.2
CASIA-B	Views	11, from 0° to 180° in 18° steps	§3.2
OU-ISIR	Gallery / probe	first sequence / second sequence per subject	§3.2
OU-MVLP	Training / test subjects	5,153 / 5,154 (pre-determined lists)	§3.2
OU-MVLP	Gallery / probe	sessions 1 / 2	§3.2
OU-MVLP	Commonly used angles	all 14, or 0°, 30°, 60°, 90°	§3.2
OU-LP Bag	Training / test subjects	29,097 / 29,102	§3.2
CASIA-E	Training	first 500 subjects; validation: 25 sequences from the last 514	§3.2
TUM GAID	Protocol	training, validation and test split from the original authors	§3.2

The CASIA-B probe-view exclusion is stated as "excluding the probe sequences with angles identical to the references" §3.2. The paper does not say how a probe is paired with a reference when the view changes. See Pitfalls.

Input size and depth of the reviewed CNNs

Source: tab. 2 §4.4.1. Layer counts are as the paper reports them.

Method	Input size	Total layers	Conv	Pool	FC
GEINet (B8)	88×128	6	2	2	2
Ensem. CNNs (B6)	128×128	7	3	2	2
MGANs (B7)	64×64	8	4	1	3
EV-Gait (B2)	128×128	9	6	0	2
Gait-joint (B10)	64×64	16	12	2	2
GaitSet (B1)	64×64	9	6	2	1
Gait-RNNPart (TBiom)	64×64	9	6	2	1
GaitPart (CVPR20201)	64×64	9	6	2	1
SMPL (ACCV)	64×64	5	3	1	1
Caps-Gait (ICPR)	64×64	9	6	2	1

Results

Author-reported numbers copied from the original papers. The survey does not rerun any method. The setup is the CASIA-B, OU-ISIR and OU-MVLP protocols in §3.2, with rank-1 accuracy §3.1. The survey says it kept only methods that followed those standard protocols §5.2. No hardware or training budgets are stated.

CASIA-B tab. 4 §5.2

NM, BG and CL are normal walking, walking with a bag, and walking with a coat. A dash means the value is not reported.

Reference	Year	Venue	NM	BG	CL	Average
TMM	2015	IEEE T-MM	78.9	—	—	—
Biom	2017	Int. J. Biom.	90.8	45.9	45.3	60.7
B6	2017	IEEE T-PAMI	94.1	72.4	54.0	73.5
DIC	2018	DIC	83.3	—	62.5	—
SOTA23	2019	PR	75.0	—	—	—
B7	2019	IEEE T-IFS	79.8	—	—	—
B10	2019	PR	89.9	—	—	—
B3	2019	CVPR	93.9	82.6	63.2	79.9
B2	2019	CVPR	89.9	—	—	—
IETBiom	2019	IET Biom.	94.5	78.6	51.6	74.9
PRL	2019	PRL	86.1	—	—	—
B1 (GaitSet)	2019	AAAI	95.0	87.2	70.4	84.2
B4	2020	IEEE T-PAMI	92.3	88.9	62.3	81.2
TIP	2020	IEEE T-IP	96.0	—	—	—
CSVT	2020	IEEE T-CSVT	92.7	—	—	—
SOTA20	2020	IEEE Access	95.1	87.9	74.0	85.7
ICPR	2020	ICPR	95.7	90.7	72.4	86.3
TBiom	2020	IEEE T-Biom.	95.2	89.7	74.7	86.5
CVPR20201 (GaitPart)	2020	CVPR	96.2	91.5	78.7	88.8
CVPR20202	2020	CVPR	94.5	—	—	—
ECCV (GLN)	2020	ECCV	96.8	94.0	77.5	89.4
ACCV (HMRGait)	2020	ACCV	97.9	93.1	77.6	89.5
MM (3DCNNGait)	2020	MM	96.7	93.0	81.5	90.4

Bold marks the best value in each column, as in the paper.

OU-ISIR tab. 5 §5.2

Reference	Year	Venue	Rank-1 (%)
SOTA3	2016	ICASSP	90.71
B6	2017	IEEE T-PAMI	92.77
SOTA5	2017	Applied Sci.	91.25
SOTA21	2018	Neuroinform.	88.56
SOTA8	2018	IEEE Access	95.67
IETBiom	2019	IET Biom.	97.40
B7	2019	IEEE T-IFS	93.20
SOTA24	2019	PRL	94.62
SOTA25	2019	IEEE T-MM	97.26
SOTA19	2020	IJCB	94.17
CSVT	2020	IEEE T-CSVT	98.93
TIP	2020	IEEE T-IP	99.27
MM	2020	MM	99.90

OU-MVLP tab. 6 §5.2

Reference	Year	Venue	Rank-1 (%)
B1 (GaitSet)	2019	AAAI	83.40
ICASSP3	2020	ICASSP	57.80
CSVT	2020	IEEE T-CSVT	63.10
TIP	2020	IEEE T-IP	84.60
ICPR	2020	ICPR	84.50
TBiom	2020	IEEE T-Biom.	84.30
CVPR20201 (GaitPart)	2020	CVPR	88.70
ECCV (GLN)	2020	ECCV	89.18

Headline numbers, as the survey states them §1.2 §5.2: 84.2% CASIA-B by GaitSet (2019); 90.4% CASIA-B average by 3DCNNGait (2020); 99.9% OU-ISIR by ref. MM; 89.18% OU-MVLP by GLN.

Field-level trends (author analysis, not measured) §5.1 fig. 7

Silhouettes are used by over 81% of the reviewed methods, skeletons by about 13%, and both by about 5% §5.1.
Gait templates are used by about 70% of the methods, and sequence volumes by about 30% §5.1.
Global feature learning covers over 87% of methods §5.1.
2D CNNs are used alone by 48% of methods; 3D CNNs and GANs by 8% each; hybrids by 26%, of which CNN+RNN is 16% §5.1.
Cross-entropy is the most used single loss (20%), followed by triplet loss (17%), contrastive loss (7%) and softmax (6%). Multiple losses are used by 41% §5.1.
CASIA-B appears in about 80% of the reviewed literature, OU-ISIR in 40%, OU-MVLP in 18%, OU-ISIR LP Bag in 5%, TUM GAID in 5% and CASIA-E in 4% §5.1.

Paper vs code

No official code.

What I checked: the paper, its abstract and the arXiv metadata have no code link or project page. A GitHub repository search for "deep gait recognition survey" returned no results. A second search for "sepas-moghaddam gait" also returned no results. Nothing was cloned, because a survey has no implementation.

Pitfalls

Survey numbers are not a fair ranking. The tables gather results from different papers with different splits, preprocessing and training budgets. The survey's own claim that it included only standard protocols §5.2 is an assertion, and the paper does not show that each row followed the protocol. Compare only within one row's source paper, or re-run the methods under one protocol.
Missing values are not zero. Many Table 4 entries have only one or two of the three conditions, and the average is then "—" tab. 4. Where all three are present, the average matches the mean of the three to one decimal place (for example B1: (95.0 + 87.2 + 70.4) / 3 = 84.2). Use the average only where the three columns are all present.
The CASIA-B probe-view rule is ambiguous. The paper says probes with an angle "identical to the references" are excluded §3.2, but it does not say which gallery angle each probe is matched to. The survey leaves this open. Read the source paper of the method you reproduce.
Section 6 in the Organization paragraph is out of date. The paragraph in §1.4 says the challenges are in Section 6. The compiled section numbering is 6 for adversarial attacks and 7 for challenges, because the adversarial section was added later. This card follows the compiled numbering, counted from the \section commands in the LaTeX source.
Layer counts are inconsistent. §4.4.1 says that "fewer than 10 layers" suffice, but tab. 2 lists Gait-joint with 16 layers. EV-Gait is listed with a total of 9 layers, but its conv, pool and FC counts add to 8.
CASIA-E year conflict. The dataset text says CASIA-E was developed in 2020 §3.2, and tab. 1 gives 2020. Yet the paper that uses CASIA-E (key SOTA23) is listed as 2019 in tab. 3 and tab. 4.
ICASSP3 year conflict. tab. 3 gives ICASSP3 as 2019, but it sits in the 2020 block of that table, and tab. 6 gives it as 2020. Check the source.
OU-ISIR Speed count does not match. The text says "nine different speeds, ranging from 2 km/h to 11 km/h, with 1 km/h interval" §3.2. Stepping 1 km/h from 2 to 11 gives 10 speeds. Treat the speed list as unverified.
The CNN resolution claim is a second-hand result. The 128×128 vs 64×64 finding is cited to ref. B6 and is not reproduced here §4.4.1.
Dataset details are thin. Several datasets are described only by a sentence (CMU MoBo, SOTON, CASIA-A). The survey says SOTON has no pre-defined protocol §3.2. Expect to read the dataset paper for exact splits.
Hybrid costs are not measured. The survey says that pose estimation adds computation §4.1 and that hybrids perform well §5.1, but it reports no runtimes, parameter counts or memory use.
Privacy and synthesis are only proposed. §7.4 suggests synthetic subjects to ease privacy concerns, but no method or dataset is evaluated.

Open problems (author-stated) §7

Disentanglement §7.1: separate identity from covariates such as view, appearance, sequence order, body-part motion and lighting. Few methods do this so far.
Self-supervised learning §7.2: learn from unlabelled gait with pretext tasks, such as body-part motion or sequence order, possibly several at once. Designing good pretext tasks is the stated difficulty.
Multi-task learning §7.3: most methods train only for identity. Auxiliary tasks such as affect, gender and age are named as options.
Data synthesis and domain adaptation §7.4: GANs or game engines could create data and reduce privacy concerns; domain adaptation could add missing variations to OU-ISIR and OU-MVLP.
Cross-dataset evaluation §7.5: no cross-dataset study on CASIA-B, OU-ISIR or OU-MVLP existed at the time. Out-of-distribution testing is suggested.
Multi-view recognition §7.6: most methods learn one view at a time; multi-view LSTM variants exist, but unknown or moving viewpoints remain open.
Multi-biometric recognition §7.7: fuse gait with face, ear and soft traits such as height, weight and age.

Lineage

Surveys it positions itself against: vision-based gait surveys covering work up to mid-2018 and a wearable-sensor survey §1.2 §1. None is linked here, because they are not in the wiki.
Methods it singles out as the state of the art: GaitSet (ref. B1) §5.2; 3DCNNGait (ref. MM) §5.2; GLN (ref. ECCV) §5.2; GaitPart (ref. CVPR20201) §5.2.
Superseded by: not stated in the paper, and not known from content in this wiki.

Source and license

Adapted from “Deep Gait Recognition: A Survey” by Alireza Sepas-Moghaddam, Ali Etemad (arXiv:2102.09546), used under CC BY 4.0. Changes: condensed and restructured for implementation, with tables, equations and algorithms taken from the paper and its code, each cited to its source. Not affiliated with or endorsed by the authors.