2V-Gait: Gait Recognition using 3D LiDAR
Robust to Changes in Walking Direction and Measurement Distance
Jeongho Ahn1

Kazuto Nakashima2

Koki Yoshino1

Abstract— Gait recognition, which is a biometric identifier
for individual walking patterns, is utilized in many applications, such as criminal investigation and identification systems,
because it can be applied at a long distance and requires no
explicit cooperation of the subjects. In general, cameras areused for gait recognition, and several methods in previous
studies have used depth information captured by RGB-D
cameras. However, RGB-D cameras are limited in terms of
their measurement distance and are difficult to access outdoors.
In recent years, real-time multi-layer 3D LiDAR, which canobtain 3D range images of a target at ranges of over 100
m, has attracted significant attention for use in autonomous
mobile robots, serving as eyes for obstacles detection andnavigation. Compared with cameras, such 3D LiDAR has rarely
been used for biometrics owing to its low spatial resolution.
However, considering the unique characteristics of 3D LiDAR,
such as the robustness of the illumination conditions, longmeasurement distances, and wide-angle scanning, the approachhas the potential to be applied outdoors as a biometric identifier.
The present paper describes a gait recognition system, called
2V-Gait, which is robust to variations in the walking directionof a subject and the distance measured from the 3D LiDAR. To
improve the performance of gait recognition, we leverage theunique characteristics of 3D LiDAR, which are not included inregular cameras. Extensive experiments on our dataset show
the effectiveness of the proposed approach.

I. INTRODUCTION
Gait recognition is a promising biometric identifier thatcan be utilized as an alternative to other approaches, such as
faces, fingerprints, and retinas, based on unique physical andbehavioral characteristics. Compared with other biometricmodalities, gait, which depicts the walking pattern of an individual, has several advantages in that it can be easily captured
at a long distance and requires no explicit cooperation of thesubjects of interest. In addition, it is difficult to camouflage
because of the gait dynamics. Owing to these advantages,
gait recognition has enormous potential in many applications,
including criminal investigations and identification systems.
Cameras are generally used for gait recognition [1], andseveral methods described in previous studies use depth
information captured by an RGB-D camera, such as a Microsoft Kinect [2]. In addition, RGB-D cameras have several
This work was partially supported by JSPS KAKENHI Grant Number
JP20H00230.
Jeongho Ahn and Koki Yoshino are with Graduate School of Information Science and Electrical Engineering, Kyushu University, Japan ahn,

yoshino@irvs.ait.kyushu-u.ac.jp
Kazuto Nakashima and Ryo Kurazume are with Faculty ofInformation Science and Electrical Engineering, Kyushu University,Japank nakashima@irvs.ait.kyushu-u.ac.jp,

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

Yumi Iwashita3

Ryo Kurazume2

Fig. 1: Overview of 2V-Gait, utilizing the unique characteristics of 3D
LiDAR that are not found in regular RGB cameras. After estimating thewalking direction of a subject point cloud, the two viewpoint-invariant gait
image and gait speed sequences are extracted and fed into the recognition
network to identify an individual.

advantages, such as robustness to the illumination conditions
and a simple background subtraction. However, the measurement distance and field of view of RGB-D cameras arelimited to approximately 10 m and 70 ◦ , respectively, andsuch cameras are difficult to be apply outdoors.
In recent years, real-time multi-layer LiDAR, which canobtain 3D range images of a target at a range of over 100
m, has attracted a great deal of attention in computer visionand has been applied in autonomous mobile robots to serveas eyes for object detection and navigation. In comparison
with cameras, however, 3D LiDAR has rarely been used inbiometrics. One possible reason for this is the poor spatial
resolution compared with RGB cameras, making it difficult
to capture the whole human shape. However, considering theunique characteristics of 3D LiDAR, such as the robustnessof the illumination conditions, long measurement distances,
and a scanning range covering 360 ◦ in the azimuth, it
has a potential outdoor application as a biometric identifier.
Furthermore, 3D LiDAR can be used as an alternative to
RGB cameras in terms of protecting personal information.
We previously proposed an LSTM-based gait recognition
method using 3D LiDAR [10] and showed that 3D LiDAR
has a high potential for such recognition. However, in thisstudy [10], both the distance and walking direction fromthe 3D LiDAR were constant. This situation is valid if
the 3D LiDAR is placed in a corridor or narrow street,and people are walking in a single direction. In the future,
however, 3D LiDAR will be widely used in practical applications, primarily mobile robots, for person identification.
For example, security robots that can be operated 24 h a
day and are less conspicuous than humans are becoming
increasingly common in malls, offices, and public spaces. A
nighttime surveillance system can be achieved by applying

biometrics to these security robots without the installation ofother sensors. Autonomous vehicles can detect and identify
specific users while driving. Based on the above cases, it isnecessary to design a robust gait recognition model for intrasubject changes, such as the viewing angles and distances
measured. To reduce the influence of these variations, we
focus on two fixed viewpoints and the walking pace, which
may be invariant gait features for the conditions above, as
shown in Fig. 1.
Our contributions can be summarized as follows:• We propose a system for gait recognition using 3D
LiDAR that is robust to variations in the walking
direction of the subject and the distance measured froma sensor.
• To improve the performance, we utilize the uniquecharacteristics of 3D LiDAR, such as 3D spatial andpositional information, which are not included in regularcameras.

(a) Overview of data collection

II. R ELATED W ORK
A. Gait Representation
Another recognition problem in computer vision is theextraction of gait-related features from the video framesof a walking person, for which most prior approaches are
separated into two types of gait representation: model-based
and appearance-based methods. Model-based methods first
apply pose estimation and take an articulated body withspatial and temporal parameters, including the skeleton,
stride length, cadence, and joint angles [3, 4].
By contrast, appearance-based features use human-shaperelated features directly from the original images. A gaitenergy image (GEI) [5] is an example of a successful
appearance-based approach. In a GEI, the spatial-temporal
information of a gait cycle is encoded into a single image.
Extended approaches, such as the frame difference frieze
pattern [6] and gait flow image [7], have been proposed
and have shown a better performance than GEI. However,these methods compress the gait sequence into a single
frame, which results in a loss of opportunity in applying the
temporal changes of gait dynamics in an explicit manner. We
focus on the appearance-based approach because it would
be difficult to maintain pose estimation accuracy enoughdue to the sparseness and incompleteness of the point cloud
captured by 3D LiDAR.

(b) Three-dimensional point cloud visualization

Fig. 2: Data acquisition environment. The subjects walked along a circularline with a radius of 5 m, the center of which was 8.5 m away from thesensor. To evaluate the robustness of our approach, the gait data extracted
through background subtraction were divided into four datasets according
to the distance measured dt .

individual or the distance measured from a sensor is notconstant. Thus, this method loses the flexibility of the gait
recognition under real-world scenarios, including variations
such as the viewing angle and measurement distance. To
solve these issues, we focus on the robustness against thewalking direction of a person and the distance measured bya sensor.

III. DATASETS
B. Person Identification using 3D LiDAR
To the best of our knowledge, only the studies by Benedek
et al. are examples of gait recognition using 3D LiDAR [9].
These studies focus on re-recognition, adopting the GEIbased method, to track a specific person for a short periodof time. However, it is difficult to extract the dynamic gaitfeatures under temporal changes, which is critical to theperformance of appearance-based gait recognition models.
In contrast to the above approach, we proposed an LSTMbased gait recognition method [10] that learns the temporal
gait changes. However, this method suffers from a degradedrecognition performance when the walking direction of an

A. Data Collection
Using 32 vertical lasers to verify the effectiveness of our
method, we collected the gait data captured using a Velodyne
HDL-32E, which creates horizontal 360◦ 3D range images.
These data consist of gait sequences from 31 subjects in apoint-cloud format. Requesting the subjects to walk as usual
along a circular line with a radius of 5 m, where the center is
8.5 m away from the sensor, the gait data contain the changes
in the 360◦ walking direction and the distance measured from
3.5 to 13.5 m, as shown in Fig. 2.

B. Creating Dataset
To evaluate the robustness of our method for changes in
the walking direction or distance measured, we constructedfour datasets by dividing the time-series gait data acquired
above, and thus the viewing angles and measurement distances are different. Let Pt be the subject point set for thetime step t, extracted by background subtraction processing:Pt = {pt,1 , pt,2 , ..., pt,N },(1)where N is the total number of points for each subject,and each point pt,n ∈ R3 indicates its orthogonal coordinates(pt,n,x , pt,n,y , pt,n,z ). Given a subject point cloud extracted, we
define the center of gravity for a subject, ct = (ct,x , ct,y , ct,z ),as follows:
1 N
ct = ∑ pt,n ,N n=1(a) Left-side view(2)

where ct,z was set to 0 to simplify the distance calculation.
Given a subject central point ct , the distance from the sensor
position o to a subject dt can be calculated as follows:
dt = kct − ok2 ,(3)Finally, the extracted time-series gait data were divided into
four subsets according to the calculated distance dt : datasets
1, 2, 3, and 4 containing gait sequences with ranges of 3.5−6
m, 6 − 8.5 m, 8.5 − 11 m, and 11 − 13.5 m, respectively.
IV. P ROPOSED M ETHOD
In this section, we propose a pipeline for our proposed
gait recognition method: gait direction transformation, input
generation, and the use of a recognition network. In particular, to enhance the robustness of changes in the walking
direction and the distance measured from a sensor, we takeadvantage of 3D LiDAR, which is not included in normal
cameras.
A. Gait Direction Transformation
We first describe how to estimate the walking directionof a pedestrian and transform the subject point cloud into
a new subject point cloud heading in a constant direction.
In other words, the walking direction of a subject point
set is transformed into the direction toward the −y-axis
and generated into left-side view gait images from the yz
coordinate plane. First, the gait directional angle for a time
step t can be calculated from the subject’s central points
before and after a time step t:
θt = arctan2(ct+1,y − ct−1,y , ct+1,x − ct−1,x ),(4)

where arctan2(·, ·) is defined as the angle between the positive x-axis in the Euclidean plane. Given a directional angle
θt , a subject point cloud transformed P̂t = {p̂t,1 , p̂t,2 , ..., p̂t,N }is obtained by rotating the original subject point cloud Pt
around ct as the z-axis.
p̂t,n = Rz (−θt − π/2) · (pt,n − ct ),(5)(b) Back viewFig. 3: Example of a generated gait image, which is one of the inputs of
the recognition network. The point cloud of the subject is sparse when thetarget is at a long distance.

where Rz represents the rotation matrix around the z-axis asfollows:
cos θ − sin θ 0
cos θ 0Rz (θ ) =  sin θ
(6)
0
0
1
The case of generating a back-view gait image follows thesame procedure, except that the rotation matrix Rz (−θt −π/2) is replaced with Rz (−θt − π).
B. Input Generation
Next, we describe how to generate the input data, which
consist of a left-side view and back-view gait image sequences, and the gait speed sequences from the pedestrian
point cloud transformed above for input into a recognition
network. The gait image it ∈ RV ×H×1 is generated from thepedestrian heading along the −y-axis:(7)
it = fimg P̂t ,where fimg (·) : RN×3 → RV ×H×1 extracts a gait image representing the depth information of a pedestrian, as shown in
Fig. 3. Here, the depth and size of the height channel areFig. 4: The overall architecture of our proposed recognition network. The gait image and speed sequence are fed into the network as inputs to learn thespatial-temporal and positional features and improve the discriminative capability of the gait recognition.

determined as V (= lz /lz-res ) × H(= ly /ly-res ) pixels, where lz ,ly , lz-res , and ly-res are the height of the z-axis, the width of they-axis, the vertical resolution of the image, and the horizontal
resolution of the image, respectively. As the criteria by which
each pixel value of the depth channel is determined, thevalue is set to 0 when none of the points correspond to the
pixel. Otherwise, it is set to the largest x-coordinate value
in the candidate set {p̂t,1 , p̂t,2 , ..., p̂t,Ñ } ⊂ P̂t , which are thepoints corresponding to the pixels in (v, h). In addition, for aclear distinction between a pedestrian and a background, theconstant lx /2 is added to the pixel value it,v,h correspondingto max (P̂t,x ) + l2x (∃p̂t,ñ )(8)it,v,h = ñ∈{1,...,Ñ}
0(otherwise)
Here, the candidate point p̂t,ñ should satisfy the following
formulation:p̂t,ñ = {p̂t,ñ ∈ P̂t | (v = ṽ) ∧ (h = h̃)},where ṽ and the h̃ are defined as follows:



1
lz
ṽ =
· p̂t,ñ,z +,lz-res
2



ly
1
· p̂t,ñ,y +h̃ =ly-res
2

(9)

(10)
(11)

It is similar to Z-buffer method which is one of the commonly used methods for hidden surface detection in terms ofcomparing surface depths at each pixel position on the projection plane. Thus, we can obtain the gait image sequence
I ∈ RT ×V ×H×1 with T frames:
I = (i1 , . . . , iT )(12)

As another input for the recognition network, the gait speed
can be obtained from the pedestrian central points before andafter time step t and the rotation rate frps of the sensor:
vt =

frps
· kct+1 − ct−1 k2
2(13)

Based on the calculation above, we can obtain the gait speed
sequence vgait ∈ RT with T frames:
vgait = (v1 , . . . , vT )(14)

Finally, we can obtain the left-side view gait image sequence
Ile f t−side , the back-viewed gait image sequence Iback , andthe gait speed sequence vgait for the proposed recognition
network. In this study, lz , ly , lz-res , ly-res , T , and frps are set
as 2.4 m, 1.6 m, 0.06 m, 0.01 m, 10, and 10 Hz, respectively.
C. Recognition Network
In this section, we describe the recognition network
for learning the discriminative information from the inputs
generated above. The overall architecture, which consistsof four key components, i.e., if density adaptive encoding(DAE), temporal feature aggregating (TFA), positional feature concatenating (PFC), and viewpoint-informed feature
aggregating (VFA), is visualized in Fig. 4.
1) Density Adaptive Encoding: A high-resolution image,which is transformed from the point set of a subject,is expected to represent fine-grained patterns of the gait.
However, this method lacks the ability to recognize thewhole human body in sparse data. Consequently, the spatial
features learned in dense data acquired at short distances
may not generalize to long distances because a point set
is generally accompanied with a non-uniform density at
different distances. To alleviate this issue, we designed adensity adaptive encoding (DAE) module that leverages the
low resolution, which may be robust to sparse data and better
recognize coarse-grained patterns, learning to combine multiscale features extracted from different resolutions.
First, the double-reduced-resolution image sequence
Ilow−res ∈ RT ×V /2×H/2×1 is obtained by feeding a gait image
sequence I into the max pooling layer:
Ilow−res = Maxpool2D(I)(15)

Then, Ilow−res is upsampled to meet the height and width
dimensions of I:
Îlow−res = Upsampling2D(Ilow−res )(16)where N is the number of subjects trained, and the onedimensional average pooling operator is adopted for effectively aggregating two of the feature vectors, which is thesame as that of RotationNet [12], as shown in Fig. 5.
V. E XPERIMENTS

Fig. 5: Detailed structure of VFA module, which aggregates the gait featuresof the recognition networks pre-trained from two different viewpoints: theleft-side and back views.

Next, two gait image sequences with different resolutions,
I and Îlow−res , are fed into the 2-D convolution network
module Conv2D(·), which shares the weights and kernel
optimizations, and obtains the spatial features, F, Flow−res ∈RT ×V /2×H/2×16 , respectively. Finally, F and Flow−res are
combined into one feature sequence F̂ using the spatial
feature fusion (SFF) operator as follows:1(17)F̂ = · (Conv2D(I) ⊕ Conv2D(Îlow−res )),2
where ⊕ represents an element-wise addition.
2) Temporal Feature Aggregating: The TFA module iscomposed of a convolutional LSTM (ConvLSTM) network[11], which is an extension of LSTM and is responsible
for modeling the spatial-temporal representations of the gait,
and a multi-layer perceptron (MLP) used to aggregate thetemporal features of F̂. ConvLSTM is expected to outperform LSTM-based approaches because it captures spatiotemporal correlations simultaneously. The spatial-temporalfeature fˆaggr ∈ R128 can be extracted by the TFA modulefrom F̂ as follows:fˆaggr = TFA(F̂)(18)3) Positional Feature Concatenating: The positional feature concatenating (PFC) module takes advantage of thewalking speed information, which can be one of the invariant
attributes for changes in distance measured from 3D LiDAR.
As shown in Fig. 4, the PFC takes the average of all frames T
in a re-weighted gait speed sequence vgait , and the extracted
gait speed feature fspeed ∈ R is concatenated into fˆaggr asfollows:
re= Concat( fˆaggr , fspeed )fˆaggr(19)4) Viewpoint-informed Feature Aggregating: The VFA
module aims to obtain more discriminative features from two
viewpoints: the left-side view and back view, by aggregating
the outputs of two networks, fle f t−side , fback ∈ RN , which are
pre-trained from two different viewpoints.
fgait = Avgpooling1D( fle f t−side , fback ),(20)

In this section, extensive experiments conducted on our
point cloud gait datasets, which contain variations in the
gait direction and distance measured from a sensor, aredescribed, the results of which demonstrate that our proposed
method is robust to such changes. The details of the dataset
and training are first described. An evaluation was thenconducted to verify the effectiveness of each component in
2V-Gait. Finally, the performance of the proposed method
was compared with that of other prior methods.
A. Implementation Details
Four point-cloud gait datasets with different gait directions
and measurement distances were used in this experiment.
Each dataset contains 31 subjects and each subject contains
210 sequences. To train the recognition network, we divided
each of the four datasets into training, validation, and test
sets with 140, 35, and 35 sequences, respectively. The
first 16 subjects were grouped into a training set, and theremaining 15 subjects were reserved as probes for testing.
In addition, we used the four training sets during the training
phase. In other words, the total training data consist of
16 × 4 × 140 = 8960 point set sequences. During the test, thethree training sets of the 15 subjects, which were not used in
the training phase, were regarded as galleries. Here, there are
four combinations in the three training sets used for learning.
During the test phase, the network identifies subjects throughthe nearest neighbor algorithm (rank-1), which computes the
cosine similarity between the gallery and probe.
For optimization, categorical cross-entropy loss was employed to train the network without any metric learningin this study. In addition, we trained the networks basedon backpropagation using RMSProp with a learning rateof 0.001, and the training batch size was set to 20 foreach person. We employ an early stopping, which is a formof regularization to avoid an overfitting during the training
phase, where the patience is set to 20.
B. Evaluation of the effectiveness in 2V-Gait
The experiment result is shown in Tab. I. First, an evaluation was conducted on our dataset to verify the effectivenessof each component in 2V-Gait, following the method of
adding modules individually. Comparing the results within
the range of 11–13 m, we achieved a better performance by
gradually adding the proposed modules. It is worth notingthat the overall accuracy was greatly improved when applying the VFA module. These results indicate that the proposed
approach is effective and robust for recognizing gait featuresin sparse data by leveraging spatial representations of two
different resolutions, the walking speed information, andtwo invariant viewpoints. By contrast, the average accuracy
decreased when the PFC module was applied. One possible

TABLE I: Averaged rank-1 accuracies on our dataset. The recognition accuracy in which the range of the test set is
not included in range of the training sets is shown in bold.
Gallery

Network

2V-Gait (ours)→ TFA

6−8.5m8.5−11m

X
X
X

X
X

X

X
2V-Gait (ours)→ TFA + DAE

X
X
X

X
X
X

2V-Gait (ours)→ TFA + DAE + PFC

X
X
X

X
X
X

2V-Gait (ours)→ TFA + DAE + PFC + VFA

X
X
X

X
X
X

GEINet [8] (Shiraga et al.)

X
X
X

X
X
X

LSTMNet [10] (Yamada et al.)Probe

3.5−6m

X
X
X

X
X
X

X
X

3.5−6m

6−8.5m8.5−11m

11−13.5m

includednon-included

X
X
X

89.90
89.33
88.76
76.76

91.40
91.59
77.44
91.01

88.57
73.52
86.10
86.48

62.67
81.71
80.57
83.24

87.39

72.60

X
X
X

89.71
89.52
88.95
71.05

91.59
89.87
85.47
91.01

89.52
71.62
87.81
87.62

68.00
82.48
81.90
83.62

87.80

74.04

X
X
X

81.33
82.86
81.14
72.57

89.29
89.29
77.44
86.04

83.62
66.86
83.05
82.10

69.71
83.05
82.48
84.76

84.26

71.65

X
X
X

92.95
91.81
92.38
81.52

95.22
95.41
89.29
95.22

94.86
89.71
95.81
95.62

76.57
91.43
90.67
91.43

93.57

84.27

X
X
X

87.05
87.81
87.43
76.57

88.72
88.53
78.59
87.19

85.71
72.76
83.24
84.95

64.38
75.43
79.81
76.19

84.34

73.08

X
X
X

74.48
73.14
74.10
67.05

76.29
73.23
69.02
71.89

70.67
59.62
69.14
68.00

51.43
64.57
65.33
65.52

70.53

61.78

X
X
X
X
X
X
X
X
X
X
X
X
X
X
X

mean

11−13.5m

reason for this is the negative effect of the walking speed
information on the spatial feature of the gait among the dense
data.

on our collected dataset demonstrate the effectiveness of the
proposed method.

C. Comparison with Prior Methods[1] Z. Zhang, L. Tran, X. Yin, Y. Atoum, X. Liu, J. Wan, and N.
Wang, “Gait recognition via disentangled representation learning,” inProceedings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition (CVPR), 2019, pp. 4710–4719.
[2] P. Kozlow, N. Abid, and S. Yanushkevich, “Gait type analysis using
dynamic Bayesian networks,” Sensors, 2018, vol. 18, no. 10, p. 3329.
[3] T. Whytock, A. Belyaev, and N. M. Robertson, “Dynamic distancebased shape features for gait recognition,” Journal of Mathematical
Imaging and Vision, 2014, vol. 50, no. 3, pp. 1–13.
[4] F. Tafazzoli and R. Safabakhsh, “Model-based human gait recognition
using leg and arm movements,” Engineering Applications of Artificial
Intelligence, 2010, vol. 23, no. 8, pp. 1237–1246.
[5] J. Han and B. Bhanu, “Individual recognition using gait energy image,”
IEEE Transactions on Pattern Analysis and Machine Intelligence,
2006, vol. 28, no. 2, pp. 316–322.
[6] M. Shinzaki, Y. Iwashita, R. Kurazume, and K. Ogawara, “Gait-based
person identification method using shadow biometrics for robustness
to changes in the walking direction,” in IEEE Winter Conference on
Applications of Computer Vision, 2015, pp. 670–677.
[7] Toby H. W. Lam, K. H. Cheung and James N. K. Liu, “Gait flow image: A silhouette-based gait representation for human identification,”
Pattern Recognition, 2011, vol. 44, no. 4, pp. 973–987.
[8] K. Shiraga, Y. Makihara, D. Muramatsu, T. Echigo, and Y. Yagi,“GEINet: related View-invariant gait recognition using a convolutional
neural network,” in 2016 International Conference on Biometrics(ICB), 2016, pp. 1–8.
[9] C. Benedek, B. Galai, B. Nagy and Z Janko, “Lidar-based gait
analysis and activity recognition in a 4D surveillance system,” IEEE
Transactions on Circuits and Systems for Video Technology, 2018, vol.
28, no. 1, pp. 101–113.
[10] H. Yamada, J. Ahn, O. M. Mozos, Y. Iwashita, and R. Kurazume,“Gait-based person identification using 3D LiDAR and long shortterm memory deep networks,” Advanced Robotics, 2020, vol. 34, no.
18, pp. 1–11.
[11] X. SHI, Z. Chen, H. Wang, D. Yeung, W. Wong and W. WOO,
“Convolutional LSTM Network: A Machine Learning Approach for
Precipitation Nowcasting” Advances in Neural Information Processing
Systems (NIPS), 2015, vol. 28.
[12] A. Kanezaki, Y. Matsushita, and Y. Nishida, “RotationNet: Joint
object categorization and pose estimation using multiviews From
unsupervised viewpoints,” in Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), 2018, pp. 5010–
5019.

Second, the performance of 2V-Gait is compared with thatof the other prior methods, i.e., GEINet [8] and an active
LSTM-based network (LSTMNet) [10]. GEINet, where a
GEI image is fed into a convolution neural network as an
input, is one of the most successful gait recognition methods,
and is equivalent with the architecture of LGEI [9]. However,
LSTMNet is an LSTM-based network that uses depth image
sequences as inputs for identifying individuals based on 3D
LiDAR. The left-side view gait image sequence Ile f t−side
was used as an input in both GEINet and LSTMNet. Here,Ile f t−side is transformed into a GEI image when GEINet
is implemented. Compared with GEINet and LSTMNet,
2V-Gait clearly presented a better performance when allcomponents were applied. In particular, our proposed approach was confirmed to be beneficial for improving thediscriminative performance for individual recognition at long
distances. From the results of the quantitative evaluations, we
showed that 2V-Gait, which utilizes unique characteristics
of 3D LiDAR, such as two different spatial resolutions, thegait speed information, and two viewpoints, improves thediscriminative capability for gait recognition.
VI. C ONCLUSION
In this paper, we presented a gait recognition method
using 3D LiDAR to improve the performance of the discrimination capability and robustness of variations in thewalking direction and distance measured. The gait image
and speed sequences were generated as gait representations
from the pedestrian point set, whose directional angle wasestimated and transformed, and fed into the recognition
network for identification. Finally, experiments conducted

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