Gait Recognition using Identity-Aware Adversarial Data Augmentation
Koki Yoshino1 , Kazuto Nakashima2 , Jeongho Ahn1 , Yumi Iwashita3 and Ryo Kurazume2

Abstract— Gait recognition is a non-contact person identification method that utilizes cameras installed at a distance. However, gait images contain person-agnostic elements (covariates)
such as clothing, and the removal of covariates is important for
identification with high performance. Disentanglement representation learning, which separates gait-dependent informationsuch as posture from covariates by unsupervised learning, hasbeen attracting attention as a method to remove covariates.
However, because the amount of gait data is negligible compared to other computer vision tasks, such as image recognition,
the separation performance of existing methods is insufficient.
In this study, we propose a gait recognition method to improve
the separation performance, which augments the training databy adversarial generation based on identity features, separated by disentanglement representation learning. The proposed
method first separates gait-dependent features (pose features)
and appearance-related covariate features (style features) fromgait videos based on disentanglement representation learning.
Then, synthesized gait images are generated by exchanging posefeatures between gait images of the person under different
walking conditions, followed by adding them to the training
data. The experiments indicate that our method can improve theseparation performance, and generate high-quality gait images
that are effective for data augmentation.

I. I NTRODUCTION
Person identification utilizing biometric data is more convenient and secure than PIN codes or handwriting, and isincreasingly being utilized as a means of identification insmartphones and ATMs. In particular, gait videos, which
comprises videos of walking people, are expected to be an effective biometric for criminal investigations and access control, because they can be obtained without contacting subjects
and without asking them to make any special movements. In
general, in gait recognition, feature vectors are calculated
based on the subject’s movement and appearance in the gait
video, and then matched with features in a database. Fromthe viewpoint of pattern recognition, gait recognition is an
open set recognition problem in which a subject class tobe evaluated may not be included in the training data, andin general, it requires high discrimination capability of theextracted gait features. One of the challenges is that gait
videos contain a lot of information that is not specific to the*This work was partially supported by JSPS KAKENHI Grant Number
JP20H00230
1 Koki Yoshino and Jeongho Ahn are with Graduate School of Information Science and Electrical Engineering, Kyushu University, Japan.

{yoshino, ahn}@irvs.ait.kyushu-u.ac.jp
2 Kazuto Nakashima and Ryo Kurazume are with Faculty ofInformation Science and Electrical Engineering, Kyushu University,
Japan.
k nakashima@irvs.ait.kyushu-u.ac.jp,

kurazume@ait.kyushu-u.ac.jp
3 Yumi Iwashita is with Jet Propulsion Laboratory, California Institute ofTechnology, USA. yumi.iwashita@jpl.nasa.gov

C

ID

G
E

G

E

E
D

Real /
Fake

Fig. 1. A schematic overview of the proposed method. The input gait videois separated into pose features fpose and style features fstyle by encoder E.
Generator G generates gait images from the pose features and style features,and discriminator D judges the authenticity of the images. The generated
images are encoded again, and classifier C identifies the subject ID basedon the pose features separated from both the real and generated image. For
evaluation, identification is conducted based on gait feature fgait , which is
the intermediate output of C.

gait (covariates), such as clothing and background, whichmakes authentication difficult.
Recently, as a method for removing covariates, disentanglement representation learning, which separates the internal
representation of a neural network according to potentiallyindependent attributes of the data, has been actively studied.
This method can learn interpretable feature representations
without the need to teach attribute labels. For example,
Zhang et al. [1] proposed a method for directly separating
and removing appearance features, which is one of thecovariates, by disentanglement representation learning. Thus,
several conventional methods have utilized disentanglement
representation learning to remove covariates. However, because the amount of gait data is negligible compared toother computer vision tasks, such as image recognition, we
considered that its separation performance was limited.
To improve the separation performance of disentanglement representation learning, we propose a gait recognition method that augments the training data by adversarial
generation, based on features separated by disentanglement
representation learning (Fig.1). The proposed method is
expected to improve the separation performances and thegeneralization performance of the feature extractor, because
the variation and amount of training data can be increased
according to the types of settings and the number of videos
in each setting of the person’s gait video. Specifically, thegait video is first separated into features that depend on
the gait (pose feature) and covariate features related to theappearance (style feature). Then, the gait images generated
by exchanging the pose features between two gait images

of the person with different settings, such as variations inclothing and walking direction, are added to the training
data. Finally, the gait features extracted from the time seriesof the pose features are utilized for recognition of thesubject. Discriminative learning and generative learning are
optimized simultaneously.
In summary, our contributions are as follows:• We propose a novel gait recognition method using
identity-aware data augmentation based on disentanglement representation learning.
• Our proposed method can generate high-quality synthesized images and improve the identification accuracy.
II. R ELATED W ORK
A. Deep Learning Based Gait Recognition
As with other computer vision tasks, there has been a great
deal of research utilizing deep learning in gait recognition.
Conventional methods have eliminated covariates by preprocessing, and can be generally classified into two categories:
model-based or silhouette-based methods. Model-based gait
recognition is a method that learns the movements of bones
and joints specific to a person, based on skeletal information
extracted by fitting a human model to gait images [2], [3].
While this method can eliminate clothing and backgroundinformation that does not depend on gait, the accuracy ofidentification is highly dependent on the accuracy of fitting
the human model, and it also removes the body shape, which
is important information for identifying individuals for gait
recognition.
In silhouette-based gait recognition, a human silhouetteis extracted from each frame of the gait video using background subtraction or segmentation methods, and the temporal change of the silhouette contour is learned to classify
the person ID. In particular, one of the most representative
silhouette-based methods is a neural network that utilizes
a gait energy image (GEI) [4] as input. GEI is an image
obtained by averaging a sequence of silhouette images of
one walking cycle over time. Because of the simplicity andefficiency of GEI, neural networks using GEI as input havebeen proposed. Takemura et al. [5] constructed a total offour types of CNNs based on metric learning, employing
contrastive loss and triplet loss as the loss function, andproposed utilizing different networks, depending on thesetting of the identification target. He et al. [6] proposed anadversarial generative network that simultaneously optimizes
two tasks: estimating the observed direction, and generating
a GEI transformed from the observed direction, and indicatedthat the proposed method is robust to differences in theobserved direction between the database and query.
However, the silhouette-based method also removes the
motion inside the contour, which is especially important infrontal and backward gait images where the contour shape
does not alter significantly. Thus, the conventional methodsof removing covariates by preprocessing have the challengeof missing information that is helpful for identification
because of preprocessing.

B. Disentanglement Representation Learning for Pedestrian
Image
Disentanglement representation learning has been attracting attention in recent years as a method to eliminate
information that is irrelevant to gait without relying on
preprocessing. Disentanglement representation learning is amethod for separating and extracting potentially independent
attributes in data as separate internal representations, without
corresponding correct labels. By applying disentanglement
representation learning to gait recognition, methods for endto-end estimation and the removal of covariate features havebeen proposed. Zhang et al. [1] introduced disentanglement
representation learning for gait recognition for the first time,and proposed a method for separating gait features andappearance features such as clothes directly from gait video.
Li et al. [7] removed gait-independent features from GEI [4]by separating person-dependent and covariate features suchas belongings and walking speed using semi-supervised
disentanglement representation learning.
Disentanglement representation learning for pedestrian images has been studied not only for gait recognition, but also
for the person re-identification (person re-id) task, which isan open set recognition problem for pedestrian images, aswell as gait recognition. Person re-id is a task to identify
a person who is captured by cameras at different locations.
Person re-id does not consider changes in walking conditions
between the gallery and probe, and therefore treats clothing
information as the main cue for person identification. Zheng
et al. [8] proposed a method that separates pedestrian images
into appearance features (e.g., clothing color and texture)and structure features (e.g., body shape and background),and replaces the separated features with other images tosimultaneously optimize data enhancement and pedestrian
identification. Eom et al. [9] proposed a method that isrobust to covariates by separating person identifying information (e.g., clothing and hair) and covariate information(e.g., posture and scale changes) from pedestrian images,
and augments the data with images generated by partiallyexchanging the separated features with another image.
As described above, disentanglement representation learning can be utilized to directly separate and extract gaitirrelevant features from gait images without preprocessing,and explicitly remove them. However, as presented in Table I,
since the dataset for gait recognition, especially the color
image dataset, generally has fewer subjects than the person
re-id dataset, the existing gait recognition methods basedon disentanglement representation learning are considered
to have inadequate separation performance, compared to theperson re-id method.
III. P ROPOSED M ETHOD
As illustrated in Fig. 1, the proposed network comprises
the following four modules: encoder E that outputs posefeatures and style features from the gait image of each frame,
generator G that generates gait images from both features
output from the encoder, discriminator D that discriminates

TABLE IC OMPARISON OF REPRESENTATIVE DATASET BETWEEN GAIT
RECOGNITION AND PERSON RE - ID
Dataset
CASIA-B [10]USF [11]Maket1501 [12]DukeMTMC-reID [13]

Task

#IDs

Gait recognition
Gait recognition
Person re-id
Person re-id

124
122
1,501
1,812

between the real image and the generated image, and classifier C, that identifies person IDs utilizing the time-series ofpose features as input.
In the proposed method, pose features are defined as timevarying and person-dependent features (e.g., body shape andjoint angles) in the time series of gait images, and stylefeatures are defined as time-invariant and person-independentfeatures (e.g., clothing and walking direction).
As a baseline for the proposed method, we adopted
GaitNet [1], which is an end-to-end feature extraction methodthat utilizes color images. As GaitNet did in the preprocessing step, the proposed method takes gait videos as
input data from which the human region is solely extracted
by the segmentation method [14].
A. Encoding from Gait Image to Pose and Style Features
First, the input gait image I (c,t) is separated and extracted
into the pose and style features by E. In I (c,t) , the value at
position c represents the walking setting, and the value at
position t represents the time.
Similar to GaitNet [1], the following pose similarity loss
(c2 ,t)(c1 ,t), fpose
Lpose-sim is calculated from the pose features fpose
obtained from two gait images of the same person withdifferent walking conditions c1 and c2 , so that the featuresof the gait images are properly separated and extracted:
Lpose-sim =

2
n1
n2
1 X
1 X
(c1 ,t)(c2 ,t)
fpose
fpose
−
.
n1 t=1n2 t=1

Generation of a synthesized gait image by exchanging
pose features between two gait images of the person
under different walking conditions
To increase the quality of the generated images to a quality
effective for data augmentation, we define two types of loss:
reconstruction loss, which contributes to the reconstructionof the original image, and adversarial loss, which mainly
contributes to the generation of synthesized gait images.
Following Zhang et al. [1], we separate and extract styleand pose features from two randomly selected frames of the
gait video at different times. The reconstruction loss Lreconis calculated from the gait image generated by inputting the(c,k)
style features fstyle , extracted from the frame at time k, and(c,l)
the pose features fpose extracted from the frame at time l
into G, and the frame I (c,l) at time l from which the posefeatures were extracted:
•

(1)

2

The loss calculated using Eq. (1) is defined such that thetime averages of the pose features of gait videos of the person
in different gait conditions approach each other, based on
the assumption that the gait videos of the person have ahigh degree of similarity in walking patterns, even if their
gait conditions are different. Accordingly, the pose features
are expected to be embedded with information that would
cause similar temporal changes among the gait videos of theperson.
B. Generation of Gait images from Pose and Style Features
Next, gait images are generated by G from the pose andstyle features extracted by E. The generated and original
images are input to D, to determine whether they are thegenerated or real images. The following two patterns exist
for image generation.
• Reconstruction of the original image from the style andpose features extracted from the gait image.

X

Lrecon =Xc∈{c1 ,c2 } k,l∈{1,...,n}
k̸=l

(c,k)

(c,l)

G(fstyle , fpose ) − I (c,l)

1

.

(2)

By minimizing Eq. (2), the image generated from the stylefeatures at time k, and the pose features at time l become
closer to the real image at time l from which the pose features
originate. This allows G to utilize common information
across frames in the style features and unique information foreach frame in the pose features when generating the image.
Specifically, the time-invariant and time-variant informationin the gait image are expected to be extracted as style andpose features, respectively.
In the reconstruction of the original image, the quality of
the generated image can be improved by the reconstruction
loss, because the original image serves as the teacher data.
However, for the generation of synthesized gait images, asimilar loss as reconstruction loss cannot be defined because
there is no supervisory data. In addition, the reconstruction
loss is the mean square error (MSE) between the original andgenerated image, and because the calculation is performed
pixel-by-pixel, the consistency of the entire image is notguaranteed.
Therefore, in this method, we define adversarial loss [15]in addition to the reconstruction loss as the loss that contributes to generative learning. By optimizing the adversarialloss, the generator attempts to produce a realistic image,while the discriminator attempts to correctly identify whether
the input image is a real or generated image. Accordingly,
the generator and discriminator are trained to compete witheach other, thus improving the quality of the generated image
without the need for correct data from the generated image.
Both real and generated images are input to D, which
computes the following adversarial loss Ladv :Ladv =

X

n 
h 
i
2
X
(i,t)
(j,t)
(j,t)
D(I
) − E D G(fstyle , fpose )−1i,j∈{c1 ,c2 } t=1

+

 

h
i
2 
(i,t)
(j,t)
(j,t)
,
D G(fstyle , fpose ) − E D(I
) +1
(3)

where E denotes the average within a mini-batch. For the definition of the adversarial loss, we adopted RaLSGAN [16],which has the most stable learning based on the results ofpreliminary experiments.
By optimizing Eq. (3), even a synthesized gait image
without a correct image can be generated with naturalquality similar to a real image. Furthermore, unlike thereconstruction loss computed on a pixel-by-pixel basis, theoptimization considers the entire image, which guarantees
consistency with the real image, and is expected to improve
the separation performance.
C. Re-encoding of the Generated Gait Image
To utilize the generated image for discriminative learning,
it is input to E again, and separated into pose features andstyle features, as described in Section III-A. To improve
the separation performance, E should always output thesimilar features for images with the similar information. The
generated image should retain the features of the original
image, and the features extracted from the generated image
should match the features of the original image. Therefore,
from the difference of the two features before and after
pose
re-encoding, the following losses Lstyleconsis and Lconsis thatguarantees the consistency of the encoder E is calculated:
Lstyleconsis =

n
X

X

(i,t)

(i,t)

(j,t)

fstyle − Estyle (G(fstyle , fpose ))i,j∈{c1 ,c2 } t=1

Lpose
consis =

n
X

X

(j,t)

(i,t)

(j,t)

fpose − Epose (G(fstyle , fpose ))i,j∈{c1 ,c2 } t=1

1

1

, (5)

D. Person Identification by Pose Features
For both real and generated images, the pose features
extracted from E are input to the classifier C, and theprobability distribution of the subject ID is the output. Theintermediate output extracted from the layer just before thefully connected layer of C, which consists of LSTM and thefully connected layer, is defined as the gait feature fgait . As
described in Section I, gait recognition is an open set recognition problem, and the learned fully connected layer cannot
be utilized for identification. Therefore, during evaluation,
the query (probe) and database (gallery) are matched by thenearest neighbor search based on the cosine similarity of the
gait features. In discriminative learning, the following crossentropy is calculated from the probability distribution of theperson ID output by C according to Zhang [1]:1XPnc∈{c1 ,c2 }nX

t=1 ωt t=1

E. Simultaneous Optimization of Generative and Discriminative Learning with Multi-task Loss
As described above, six losses are defined in the proposed
method. Zheng et al. [8] proposed a person re-id method
based on data augmentation using separated features, andreported that both the quality of the generated images andthe identification accuracy can be improved by simultaneously optimizing the generative and discriminative learning
based on the experimental results. Therefore, our method
simultaneously optimizes the above six losses in the learning
process with the following multi-task loss L:
L = λrecon Lrecon + λpose-sim Lpose-sim + λid Lid



(c,1)
(c,t)
−ωt y T log C(fpose , ..., fpose )

!
.
(6)

Note that y is the correct label, ωt is the weight for theidentification result, and ωt = t2 was adopted according to

(7)

style
style
pose
+ λposeconsis Lconsis + λconsis Lconsis + λadv Ladv ,where λ∗ is a hyperparameter that adjusts the effect of eachcorresponding loss L∗ .
IV. E XPERIMENTS, (4)where Epose (∗) refers to the pose features, and Estyle (∗)
refers to the style features of the output E(∗) from theencoder.
By optimizing Eq. (4), (5), we aim to ensure that thefeatures of the image from which the separated features
originate are accurately retained in the generated image.

Lid =Zhang’s method [1]. The loss in Eq. (6) is weighted by ωtaccording to the number of frames in the input gait video,
based on the assumption that the longer the duration of the
input gait video, the higher the discriminability of the gait
video.

In the experiments, we utilized the CASIA-B [10] dataset
to quantitatively and qualitatively evaluate the quality of thegenerated images. CASIA-B comprises three image sets: NM(normal), BG (with a bag), and CL (in a long coat). For
training, we utilized the walking images of 74 subjects in thefirst half of the dataset. In addition, 20 consecutive frames
were randomly cut from input gait video for training, and all
the frames in the test dataset were used for evaluation.
Table II presents the methods adopted in the experiments.
To verify the effectiveness of each component of the proposed method in detail, we performed experiments not onlywith the proposed method and the baseline, but also withthe method in which each component was removed one
by one from the proposed method. Note that the baseline
utilized in this experiment was implemented by us basedon the study by Zhang [1]. Adaptive instance normalization(AdaIN) [17] is a normalization process that is widelyused in style transformation tasks to improve computational
efficiency, and has been added to the proposed method as
denotes only thosean improvement from the baseline. Lfake
id
Lid defined by (6) for the generated image (i.e., w/o Lfake
id
TABLE IIC OMPARISON METHODS
MethodBaselineOurs
w/o Ladv
w/o Lfakeid
w/o Lconsis
w/o AdaIN

Ladv

Lfake
id

pose
Lstyleconsis , Lconsis

AdaIN [17]

✓

✓
✓

✓
✓
✓

✓
✓
✓
✓

✓
✓
✓

✓
✓

✓

is the setting in which the generated image is not utilized astraining data).

fstyleA. Quantitative Evaluation of Generated Image Quality

fpose

In this experiment, we randomly selected 5,000 images
from all the images in the dataset and quantitatively evaluatedthe quality of the generated images by using Fréchet inception distance (FID) [18] for the images generated in random
combinations. FID is widely used to measure the quality ofgenerated images, and the smaller its value, the higher thequality of the generated images. Table III presents the resultsof the FID measurements for each method. The smallest
value is bolded and underlined, and the second smallest value
is underlined.
Table III indicates that the quality of the generated images
is higher for the proposed method than for the baseline.
Comparing the values for each method, the quality is improved only when the generated image is not used fordiscriminative learning (w/o Lfakeid ), but the quality decreases
are removed from thewhen the factors other than Lfake
idproposed method. In particular, when the adversarial loss
is removed (w/o Ladv ), the FID is larger than the baseline,
indicating that the adversarial loss contributes significantly
to the improvement of the generated image quality in the
proposed method.

BaselineOurs
w/o Ladv
w/o Lfakeid
w/o Lconsis
w/o AdaIN
Fig. 2. Generated images by exchanging pose feature between two gait
images of the person under different walking conditions. The first andsecond lines are the source images of the style and pose features in thegenerated images, respectively. The third column indicates the enlargementof a part in the second column for a detailed verification of the pose
reproducibility.

B. Qualitative Evaluation of Generated Image Quality
The images generated by exchanging the pose features
between two images of the person under different walking
conditions are illustrated in Fig. 2. If the separation performance of the disentanglement representation learning ishigh, we expect to generate an image with the pose of thesecond row, but the clothing of the first row. In the second
column, the leftmost two rows are images with the sameshooting angle between rows 1 and 2 (fstyle and fpose ), andthe rightmost two rows are images with different shooting
angles.
As illustrated in Fig. 2, the outline and color of the image
generated by the baseline are blurred. In addition, the detailsof the baseline indicated in the third column, such as thelength and angle of the leg and arm, are different from thosein the original image of the pose features. This indicates thatthe baseline can separate and extract features with blurredcolors and contours when the shooting angles of the original
images of the pose and style features are similar, but when
the shooting angles are different, the separation performance
TABLE IIIC OMPARISON OF FID
Methods

FID

BaselineOurs
w/o Ladv
w/o Lfakeid
w/o Lconsis
w/o AdaIN

169.8
118.7
205.7
106.3
138.6
130.5

is degraded. In contrast, the proposed method can separate
and extract the features without blurring the contours andcolors, even when the shooting angles of the source imagesof both features are different.
The results of the method with each element removed
from the proposed method indicate that the setting withadversarial loss removed (w/o Ladv ) results in a generated
image similar to the baseline, indicating that adversarial loss
contributes significantly to the improvement of the separation
performance. In the setting where the generated image is not
utilized for discriminative learning (w/o Lfakeid ), which gives
the best results in the quantitative evaluation of the generated
image quality, the generated image clothing is contaminatedwith elements of the second row image (source of posefeature), which suggests that the separation performance isworse than the proposed method.
C. Quantitative Evaluation of Identification Accuracy
In the experiments for quantitative evaluation of the discrimination accuracy, we calculated the rank-1 identification
accuracy (the percentage of IDs in the nearest neighbordata in the database (gallery) that match the correct ID) for
three settings: NM, BG, and CL. Following the experimental
protocol as well as the baseline authors [1], we used NM gait
videos of all angles as the gallery, except for the angle of theprobe to be evaluated, and calculated the rank-1 identification
accuracy for each angle of the query (probe). In all settings,
the evaluation is performed using a common learned model.
The average values of rank-1 identification accuracy for
all angles in NM, BG, and CL are presented in Table IV.

TABLE IV
R ANK -1 IDENTIFICATION ACCURACY [%]This is considered to be because of insufficient reproducibility of the baseline.

Methods

NM

BG

CL

R EFERENCES

Baseline (official announcement [1])Baseline (our reimplementation)Ours
w/o Ladv
w/o Lfakeid
w/o Lconsis
w/o AdaIN

91.6
90.2
94.4
91.2
93.3
91.3
92.7

85.7
86.1
88.8
87.7
87.3
86.9
86.8

58.9
24.3
29.2
27.2
27.4
29.2
29.1

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The largest values are bolded and underlined, and thesecond largest values are underlined. The values publishedby Zhang [1] are also included in each table to verify
the reproducibility of the baseline implementation in thisexperiment.
Table IV indicates that the proposed method improves
the identification accuracy in all settings, and is the highest
accuracy among all the methods compared. In addition, thediscrimination accuracy of the proposed method is improvedby each factor, especially in the settings where adversarial
loss is removed (w/o Ladv ) and where consistency loss isremoved (w/o Lconsis ), the values of which are significantly
less than those of the proposed method. This indicatesthat the contribution of adversarial and consistency losses
is particularly significant in the identification accuracy ofthe proposed method. However, in CL, all the methods we
implemented, except for the baseline indicated in [1] havean accuracy of approximately 30%, which is less than theother settings.
Based on the comparison with the published values, we
believe that the reason for this is the insufficient reproducibility of the reimplemented baseline in this study. For NM and
BG, the accuracy of the published values and the baseline
are close, while for CL only, the baseline is approximately
30% smaller than the published values. This suggests thatthe reason for the lower accuracy only for CL is that our
implementation of the baseline in this study is not sufficiently
reproducible.
V. C ONCLUSION
In this study, we present a novel gait recognition methodthat combines disentanglement representation learning anddata augmentation to improve the separation performanceof disentanglement representation learning. In the proposed
method, gait images are encoded into the pose and style features. Synthesized gait images are generated by exchanging
the pose features between the videos of the person under
different walking conditions, and added to the training data.
The experiments indicate that the proposed method improves
the quality of the generated images and the performance offeature separation in disentanglement representation learning, and the synthesized images generated by the proposed
method are effective for data augmentation. However, theidentification accuracy is solely inadequate in the setting
where the clothing is different between database and query.