Gender Classiﬁcation Using Video Sequences ofBody Sway Recorded by Overhead Camera
Takuya Kamitani∗ , Yuta Yamaguchi∗ , Shintaro Nakatani∗ , Masashi Nishiyama∗ and Yoshio Iwai∗∗ Graduate School of Engineering, Tottori University, Tottori, 680–8552, Japan

Abstract—We investigate whether it is possible to classify thegender of a standing person based on a video sequence containing
body sway recorded by an overhead camera. Existing methods
that extract a feature from the movement of a walking personfor gender classiﬁcation cannot detect the slight movements ofa standing person. In this paper, we propose a method forextracting a feature from the body sway of a standing person.
We design a spatio-temporal feature for representing body sway
using the frequency analysis of time-series signals derived fromthe local movements of the upper body. To evaluate the accuracyof our method, we acquired video sequences of body sway from30 females and 30 males using an overhead camera. We foundthat our method obtained 90.3±1.3% accuracy for the genderclassiﬁcation of a standing person. We compared the accuracy of
our method with that of parameters based on medical data. We
found that the proposed spatio-temporal feature extracted frombody sway signiﬁcantly improved gender classiﬁcation accuracy.

I. I NTRODUCTION
There is high demand for technology that can classify thegender of a person based on a video sequence [1], [2], [3].
Such gender classiﬁcation has various applications, such as
security surveillance and marketing planning. To accurately
classify the gender of a person, the characteristics that distinguish between females and males must be obtained. The
movements of a person in a video sequence have recently
been considered for representing such characteristics.
In general, the movements of a person can be divided
into walking movements (gait) and standing movements (bodysway). Below, we review methods that classify the gender ofa person based on walking or standing movements in a video
sequence. We ﬁrst consider gender classiﬁcation based on gait.
To distinguish between females and males based on gait, somemethods [4], [5], [6] extract the gait energy image (GEI) asa feature for training a gender classiﬁer. It has been reported
that GEIs can be used to classify the gender of a walking
person with high accuracy. However, methods based on GEIs
are designed for classifying the gender of a walking person. To
the best of our knowledge, there are no existing methods forgender classiﬁcation based on body sway. Here, we propose amethod for extracting a feature from body sway and investigate
whether it can be used for gender classiﬁcation.
We discuss whether body sway can be used to distinguishbetween females and males. Analytical research in the medical
ﬁeld has shown that there are differences between standing
females and males in terms of body sway. Analytical studies [7], [8], [9] have used time-series signals of the center
positions of the pressure of the feet acquired from a force

plate placed on the ﬂoor. They demonstrated that there are
signiﬁcant differences between females and males in termsof the frequency characteristics and trajectories of the timeseries signals. These studies focused on obtaining medical dataon body sway and did not consider practical applications. To
apply such medical data for gender classiﬁcation, a contacttype sensor must be placed on the ﬂoor.
Here, we observe body sway using a camera, which is noncontact-type sensor. Previous studies [10], [11], [12], [13], [14]have measured body sway using a camera instead of a force
plate for applications such as fall prevention assessment, avatar
video generation, and person re-identiﬁcation. However, thefeatures of body sway were not used to distinguish between
females and males.
Here, we investigate whether body sway can be used toclassify the gender of a standing person by extracting a featurefrom a video sequence. We used an overhead camera attachedto the ceiling in our experimental setting. We assumed thatthe head of a standing person makes larger movements thanthose of the legs and waist. An overhead camera can observe
upper body sway, including that of the head. In our method, we
estimate the upper-body region in a video sequence to obtaina silhouette sequence. We measure the time-series signalsof body sway from the silhouette sequence and extract afeature for gender classiﬁcation. We created a dataset of video
sequences of the body sway of 60 participants to evaluate
gender classiﬁcation accuracy. We found that our method
obtained 90.3 ± 1.3% accuracy for gender classiﬁcation on
our dataset. We also compared the accuracy of our methodwith that of features derived from medical data and foundthat our method has superior accuracy. To the best of ourknowledge, the use of body sway in video sequences forgender classiﬁcation has not been previously reported. Our
main contribution is the development of a method for genderclassiﬁcation based on body sway. The remainder of thispaper is organized as follows. Section II reviews related work.
Section III describes our method and Section IV shows theexperimental results of gender classiﬁcation. Finally, Section V
presents the conclusions.
II. R ELATED W ORK
A. Video Sequences of Walking People for Gender Classiﬁcation
To classify the gender of a walking person in a video sequence, some methods [4], [5], [6] use GEI features extracted
from gait. A GEI feature [15] is represented by an averageimage calculated from a silhouette sequence containing the
movements of arms and legs during one gait cycle. Shan etal. [4] applied GEI features directly to gender classiﬁcation.
Martı́n-Félez et al. [5] temporally divided one gait cycle
into four intervals and extracted a GEI feature from eachinterval for gender classiﬁcation. Yu et al. [6] assumed thatthe movements of arms and legs affect gender classiﬁcation
accuracy. Their method extracts a GEI feature that represents
the movement of each body part and assigns an adaptive
weight to each feature. Various methods [4], [5], [6] assume
that arms and legs provide the most information. However,the body sway of a standing person rarely includes large
movements of the arms and legs. In this paper, we extract
a feature from body sway for gender classiﬁcation.
B. Use of Single Images for Gender Classiﬁcation
Some methods extract features from a single image forgender classiﬁcation. Studies [16], [17], [18], [19] have proposed the use of low-level features derived from the colors
and gradients in a single image. Other studies [20], [21],[22], [23] applied a convolutional neural network (CNN)
to extract features from a single image in an end-to-end
framework. These methods achieve high accuracy in genderclassiﬁcation when trained using a large number of images.
Here, we increase gender classiﬁcation accuracy by incorporating single images with temporal movements. Convolutional
three-dimensional (C3D) [24] features are well-known spatiotemporal features. Xu et al. [25] and Liu et al. [26] reported
that C3D features are useful for action recognition for classifying large movements, such as soccer shots, table tennis
shots, and swimming strokes. However, C3D features are notdesigned for gender classiﬁcation. We thus extract a spatiotemporal feature from body sway that are suitable for genderclassiﬁcation.
C. Analytical Research on Differences between Female andMale in Terms of Body Sway
Analytical studies [7], [8], [9] have been conducted todetermine the differences between females and males in termsof body sway. These studies obtained time-series signals ofbody sway using a force plate placed on the ﬂoor and reported
that there are signiﬁcant differences in these signals between
females and males in terms of frequency characteristics andtrajectories [7], the elliptic approximated from trajectories [8],and the speciﬁc band of frequency characteristics [9]. However, they did not apply these parameters to gender classiﬁcation. In preliminary experiments, we found that we could
not achieve high gender classiﬁcation accuracy using medical
data. We thus extract a feature from body sway to accurately
classify the gender of a standing person.
D. Applications of Body Sway in Video Sequences
Using a camera instead of a force plate to measure thebody sway of a standing person has various applications [10],[11], [12], [13], [14]. Wang et al. [10] used body sway toevaluate the risk of falling. They observed a person fromvarious directions using multiple cameras and obtained thetime-series signals of three-dimensional centers. Nishiyama etal. [11] used body sway to generate a video sequence of anavatar of a person. They observed a person using a camera
placed in front of the person and estimated the center position
from time-series signals. Yeung et al. [12] and Lv et al. [13]applied body sway to evaluate a person’s balance in the clinicalﬁeld. They analyzed the time-series signals of body joints
obtained from Microsoft Kinect. Kamitani et al. [14] applied
body sway to identify people. They obtained the time-series
signals of body sway recorded by an overhead camera andextracted the feature representing the identity of an individual.
The above examples demonstrate that body sway can be usedfor various applications. However, body sway has not been
applied to gender classiﬁcation. In this paper, we investigate
whether body sway can be used to classify the gender of astanding person.
III. P ROPOSED G ENDER C LASSIFICATION M ETHOD
A. Overview
The proposed method can classify the gender of a standing
person using a video sequence of body sway. We acquirea video sequence of a standing person using an overheadcamera attached to the ceiling. The overhead camera is used
to observe the upper body of a standing person, where theamount of movement is larger than that of the lower body.
For the upper body, the head has the largest movement. We
extract an informative feature from the upper body for genderclassiﬁcation by acquiring a video sequence of body sway.
Here, we discuss the camera setting used to view the upper
body of a standing person. Ceiling height, which varies inreal-world scenarios, affects the apparent size of a person andthus the amount of the movement observed from body sway.
Fig. 1 shows examples of the apparent size of the upper body.
Although Figs. 1 (a) and (b) show the upper body of the same
female, the apparent sizes are completely different because
the ceiling heights are different. The same tendency for males
is shown in Figs. 1 (c) and (d). We thus develop a method
for body sway measurement that does not depend on ceiling(camera) height.
The proposed method consists of the following three steps.
We assume that a person is standing below the overheadcamera and maintains the same posture. Fig. 2 shows an
overview of the proposed method. In the ﬁrst step, we acquirea video sequence of the standing person using the overheadcamera and use it to estimate a silhouette sequence that
represents the upper body. In the second step, we remove
the variation of the apparent size of the upper body in thesilhouette sequence due to the height of the overhead camera.
In the third step, we measure the time-series signals of bodysway from the silhouette sequence and extract a feature forgender classiﬁcation. We determine the gender class using theextracted feature and a pre-trained classiﬁer. We describe theremoval of the variation in the apparent size of the body regionin Section III-B and the extraction of a feature from body sway
for gender classiﬁcation in Section III-C.

High ceilingLow ceiling(a)Video sequenceof body sway
Female
(a)

(b)

(c)

(d)

Fig. 3. Examples of silhouette sequence frames estimated from video
sequences. The overhead camera was set at different heights. Female recordedby (a) low and (b) high camera. Male recorded by (c) low and (d) high camera.
Black and white pixels respectively represent the upper body and background.

Female(b)High ceiling

Input: silhouette sequence containing size variation

Low ceiling2.1. Cropping of rectangular areathat includes body region fromeach silhouette frame

FemaleMale

Rectangular area

2.2. Application of scaling to therectangular area
Male
(c)

Male
(d)

Scaled body region
Output: silhouette sequence without size variation

Fig. 1. Examples of the variation of the apparent size of the upper bodyin our experimental setting, where the camera height was randomly changed.
Female recorded by (a) low and (b) high camera. Male recorded by (c) low
and (d) high camera.

Input: video sequence recorded by an overhead camera
1. Estimation of silhouette sequence
from video sequence

Silhouette sequence
2. Removal of variation of apparent
upper body size
Upper body size
without variation
3. Measurement of time-series signalsof body sway and extractionof features for gender classification

LM feature

Output: gender class predicted using a classifierFig. 2. Overview of proposed three-step method for gender classiﬁcation.
The input is a video sequence containing body sway recorded by an overheadcamera and the output is a gender class predicted using a classiﬁer and an
extracted feature.

B. Removal of Variation in Apparent Size of Person in Silhouette Sequence
To accurately classify gender, the intra-class variation of
appearance should be small. However, the apparent size ofthe upper body can increase this variation when the heightof the overhead camera varies, as described in Section III-A.
A silhouette sequence is also affected by the variation of
apparent size. Fig. 3 shows examples of the apparent size of
silhouette sequences of the upper body. The frames of thesilhouette sequences in Figs. 3 (a) and (b) are estimated from

Fig. 4. Removal of the variation of the apparent size of the upper body in asilhouette sequence.

the same female but different camera heights. Although thesilhouette sequences both belong to the female class, theirapparent sizes of the upper body are different. The sametendency can be seen for the male class in Figs. 3 (c) and(d). If we do not consider the variation of the apparent size,
gender classiﬁcation accuracy will be low because the intraclass variation of appearance will be large.
We thus remove the variation of the apparent size in our
method, as shown in Fig. 4. In this step, we crop a rectangulararea from each input silhouette frame to remove the background region. The rectangular area includes the upper bodyand has a margin to prevent cutting off the upper body. We set
this margin based on the maximum amount of movement. We
determine the margin for each silhouette sequence. Finally, weapply a scaling technique so that the height and width of therectangular area are equal to the reference values H and W ,respectively.
C. Extraction of a Feature from Body Sway for GenderClassiﬁcation
We now describe the extraction of a spatio-temporal featurefrom body sway for gender classiﬁcation. Our method is
inspired by the framework of an existing method [14] for person re-identiﬁcation. Fig. 5 shows the feature extraction step.
The upper body in each silhouette frame is radially divided
into I local blocks to extract a spatial feature. Subtractions
between a reference silhouette frame and silhouette frames arecalculated to extract a temporal feature. We now describe thedetermination of the reference silhouette frame. The distances
between all silhouette frames are calculated. The reference
silhouette frame with the smallest distance is selected. In eachInput: silhouette sequence

Time-series signals ofbody sway

LM feature

Fig. 5. Feature extraction step. An LM feature is extracted from a silhouettesequence of body sway.

IV. E XPERIMENT
A. Dataset
We evaluated whether the gender of a standing person
can be classiﬁed based on a video sequence of body sway
recorded by an overhead camera. We acquired video sequencesof the body sway of 60 participants (30 females and 30
males). Table I shows the details of the participants. The
same instructions were given to all participants. We asked
the participants to maintain an upright posture (Romberg’spose), shown in Fig. 6 (a), during the acquisition of their
video sequence. We assumed a scenario where people wear
the same work clothes in a factory. To reduce the changes inface orientation during the acquisition of a video sequence,
we asked all participants to keep looking at a timer placed 3.0
m away. We set the height of the timer at 1.4 m. Fig. 6 (b)shows the experimental setting for the acquisition of video
sequences of body sway. We randomly set the height of theoverhead camera to between 2.0 and 4.0 m from the ﬂoor.
The resolution and sampling rate of the overhead camera were1920 × 1080 pixels and 30.0 Hz, respectively. We calibrated
the overhead camera such that the optical axis coincided withthe direction normal to the ﬂoor. The internal parameters of
the overhead camera were ﬁxed. We set the time length ofeach video sequence to 60 s. Fig. 7 shows examples of color
images of females and males in video sequences acquired inVideo sequenceof body sway
Timer

Participant
3.0 m

Male

Female(a)local block, the amount of movement is calculated by summing
the absolute values of all subtractions to obtain the time-series
signals. Then, a window function of length L is convoluted
into the time-series signals of the amount of movement in each
local block. The power spectral density (PSD) [27] is estimatedfrom the time-series signals. PSD consists of the component of
the power value corresponding to each frequency. The numberof components of PSD in each local block is L/2. Finally, thePSDs of all local blocks are combined into a feature vector for
gender classiﬁcation. The dimension of the feature is IL/2.
The vector of PSDs is denoted as a local movement (LM)feature.

Male
30
21.6 ± 1.3
170.2 ± 6.4

Overheadcamera3.3. Application of frequency analysis
to time-series signals
Output: feature as input for a classifier

Female30
22.4 ± 6.3
158.7 ± 4.7

1.4 m

3.2. Acquisition of time-series signals
based on the amount of
movement in each local block

Local blocks

Number of participants
Average age (years)
Average height (cm)

2.0 m − 4.0 m

3.1. Radial division of upper body ineach silhouette frame into
local blocks

TABLE ID ETAILS OF THE PARTICIPANTS IN OUR DATASET OF VIDEO SEQUENCESCONTAINING BODY SWAY.

(b)

Fig. 6. Experimental setting for observing participants using an overheadcamera. (a) Examples of a female and a male standing with an upright posture.
(b) Camera setting for acquiring a video sequence of body sway.

our experimental setting. The apparent size of the upper bodyin the color images varies because of differences in camera
height. The inter-class variation between females and males
is small even though the intra-class variation of the apparent
sizes is large.
B. Evaluation of Gender Classiﬁcation Accuracy
We compared the accuracy of our method with those of threeother methods in our experiment. The details of the methods
are as follows.
Proposed method (LM): We used the LM features describedin Section III to represent the body sway of a standing person.
To extract an LM feature, we set the number of local blocks
to I = 8 and the length of a window function to L = 64 (2.1s). We estimated a silhouette sequence from a video sequence
using a conventional background subtraction technique. We
set H = 100 pixels and W = 100 pixels. We applied a linear
support vector machine (SVM) as the classiﬁer and set its
regularization parameter to C = 1.0.
Alternative method 1 (GEI): We used the GEI [15] features
reported in previous studies on the gender classiﬁcation ofa walking person. To extract a GEI feature, we calculated
a temporal average image of all frames in a 60-s silhouettesequence. We used the same silhouette sequences as those in
our method. We applied a linear SVM as the classiﬁer and set
its regularization parameter to C = 1.0.
Alternative method 2 (CNN): We used a CNN [22] withsingle images as a representative of conventional classiﬁcation
techniques. The structure of the CNN consisted of four twodimensional convolutional layers and four two-dimensional
pooling layers. We used 45000 images of females and 45000
images of males as training samples for the CNN. The size ofthe sample images was set to 100 × 100 pixels. Each pixel had
RGB color values. Binary cross-entropy with the stochastic
gradient descent was used.

Accuracy (%)

100

60

Proposedmethod(LM)

Alternative
procedure 1(GEI)

Alternative
procedure 2(CNN)

Alternative
procedure 3(C3D)

Fig. 8. Comparison of gender classiﬁcation accuracy obtained using proposed
LM, GEI, CNN, and C3D features.

Females

Males
Fig. 7. Examples of color images of females and males in video sequences
acquired by an overhead camera. These images show variation in the apparent
size of the upper body due to camera height. The inter-class variation between
females and males is small even though the intra-class variation of the apparent
size is large.

Alternative method 3 (C3D): We used C3D [24] with shortvideo sequences as a representative of spatio-temporal feature
extraction. The structure of C3D consisted of four threedimensional convolutional layers and four three-dimensional
pooling layers. We used 2800 short video sequences of females
and 2800 short video sequences of males as training samples.
Each sample of a video sequence consisted of 16 frames. The
size of each frame was set to 100 × 100 pixels. Each pixel ofa frame had RGB color values. Binary cross-entropy with thestochastic gradient descent was used.
We randomly shufﬂed 60 participants and selected 50
participants as training samples and 10 participants as test
samples. We completely separated the participants between
the training samples and the test samples. We conducted therandom shufﬂing 30 times and calculated the average andstandard deviation of the gender classiﬁcation accuracy foreach method.

Fig. 8 shows the gender classiﬁcation accuracy for eachmethod. The accuracy of our method is much higher than thatof GEI features. GEI features were designed to represent thelarge movements of the arms and legs during walking andthus cannot accurately represent the slight movements of astanding person. Conversely, the proposed LM features were
designed to represent body sway. They thus have higher genderclassiﬁcation accuracy compared with that of GEI features.
Furthermore, the accuracy of our method was superior tothat of the CNN. The CNN extracted only spatial featuresfrom single images. It was not designed to extract temporalfeatures from movement. We used C3D features to extract
spatio-temporal characteristics. The proposed LM featuresoutperform these features. C3D features were not designed torepresent body sway (their target is large movements during
large movements) and thus have relatively low gender classiﬁcation accuracy. The proposed LM features include better
spatio-temporal characteristics for representing body sway.
C. Visualization of SVM Weights Calculated from LM Features
We visualized the SVM weights calculated when training agender classiﬁer to determine the most informative componentof the proposed LM features for gender classiﬁcation. We used
I = 8 local blocks to extract an LM feature (see Section IV-B).
The local blocks are labeled P1 to P8 as shown in Fig. 9 (a).
Local blocks P2, P4, P6, and P8 correspond to the left hand,back, right hand, and face, respectively. Fig. 9 (b) shows the
SVM weights of the LM features corresponding to the local
blocks. The horizontal axis represents the frequency in each
local block. The extreme left and right on the horizontal axisof each local block represent DC and 15 Hz, respectively.
The vertical axis represents the weight of each component. A
component with a negative (positive) weight contributes to theclassiﬁcation of females (males). The number of components
of an LM feature was I × L/2 = 8 × 64/2 = 256.
First, we identiﬁed the most informative local block forgender classiﬁcation. We calculated the sum of the absolute
SVM weights in each local block. The sum for local blocks
P1 to P8 was 2.65, 1.72, 2.51, 3.44, 2.36, 2.36, 2.29, 2.11,
and 1.81, respectively. A local block was more informative forgender classiﬁcation when its sum was higher. Local block P4

P7

P1

P2

Weights of
SVM

P8
P6

P1

P2

P3

P4

P5

P6

P7

P8

0

-0.32
(Female)

P4
P3

0.32
(Male)

P5(a) Definition of local blocks

DC

15 DC

15 DC

15 DC
15 DC
15 DC
15 DC
15 DC
15
Frequency in each local block (Hz)(b) Visualization of SVM weights of LM features corresponding to each local block

Fig. 9. Visualization of SVM weights for determining the most informative component of proposed LM features for gender classiﬁcation. (a) Deﬁnition of
local blocks P1 to P8. (b) SVM weights of LM features corresponding to the local blocks.

P4

Female 1
Female 2
Female 3

PSD

6.5

Male 1
Male 2

Male 3DC
15
Frequency (Hz)(a) Component of LM feature corresponding to local block P4
-4.5

PSD

6.5

P2

Female 1

Female 2
Female 3
Male 1
Male 2

Male 3DC
15
Frequency (Hz)(b) Component of LM feature corresponding to local block P2-4.5

Fig. 10. Examples of LM features corresponding to local block P2 or P4 forthree females and three males.

(corresponding to a person’s back) had the highest sum andwas thus important for gender classiﬁcation.
Next, we identiﬁed the most informative frequency band in
local block P4 for gender classiﬁcation. The high-frequency
band is more informative than the low-frequency band for classifying the female class using the SVM weights corresponding
to P4 in Fig. 9 (b). To determine the differences in LM features
between females and males, some examples of the features
corresponding to P4 are shown in Fig. 10 (a). Examples of
those corresponding to P2 are shown in Fig. 10 (b). Local
block P2 was not discriminative because it had the lowest
sum of absolute SVM weights. We compared the features of
P2 with those of P4. In P4, there are large differences in thehigh-frequency band (3.0 to 15.0 Hz), in which LM features offemales are higher than those of males. Conversely, there are
no remarkable differences between females and males in P2.
We now discuss the reasons for the differences in P4. Local
block P4 contained the back of the head, where females often
have longer hair. We believe that the differences in the highfrequency band appear because the long hair moved during
body sway.

D. Gender Classiﬁcation Accuracy Obtained using MedicalData
We evaluated the gender classiﬁcation accuracy of parameters derived from medical data. As described in Section II-C,
analytical studies [7], [8], [9] have reported that the differences
between females and males can be observed in the frequency
characteristics and trajectories of time-series signals. Note thatthese studies used a force plate to acquire the parameters. In
our experiment, we used an overhead camera instead of a force
plate to acquire the time-series signals of the center positionsof the upper body using silhouette sequences.
We used ten parameters, namely F1 to F6 for frequency
characteristics and T1 to T4 for trajectories, reported inprevious studies [7], [8], [9]. The parameters were set as
follows:
F1 [7]: DC component of the power spectrum of center
positions.
F2 [7]: Top accumulated frequency component of the power
spectrum of center positions.
F3 [8]: DC component of the power spectrum of velocities.
F4 [9]: Sum of the vertical power spectrum at frequencies
lower than 0.2 Hz.
F5 [9]: Sum of the horizontal power spectrum at frequencies
lower than 0.2 Hz.
F6 [9]: Sum of the vertical power spectrum at frequencies
higher than 2.0 Hz.
T1 [8]: Area of an ellipse approximated to the trajectory ofcenter positions.
T2 [8]: Length of the major axis of the ellipse.
T3 [8]: Length of the minor axis of the ellipse.
T4 [8]: Length of the trajectory of center positions.
We tested each parameter as a one-dimensional feature
vector for evaluating gender classiﬁcation accuracy. We also
combined the parameters into a ten-dimensional feature vector (All) to improve accuracy. The experimental conditions,
except for the features, were the same as those for our method
in Section IV-B.
Fig. 11 shows a comparison between the accuracy of eachparameter derived from medical data and that of the proposed
LM feature. The proposed LM feature had higher accuracy.
The accuracy of the combined parameters was higher than thatof individual parameters. The results show that the proposed

Accuracy (%)

100

20

LM F1

F2

F3

F4

F5

F6

T1

T2

T3

T4

All

Fig. 11. Accuracy of parameters F1-F6 and T1-T4 derived from medical
data and proposed LM feature. ‘All’ represents a feature that combines allparameters.

LM feature has higher gender classiﬁcation accuracy than thatof parameters derived from medical data.
V. C ONCLUSIONS
We investigated whether the gender of a standing person can
be classiﬁed by extracting a feature that represent body sway in
a video sequence recorded by an overhead camera. Our method
normalizes the apparent size of silhouette sequences of theupper body to remove variation. We divided the upper body
into local blocks to represent spatial features and measured
the time-series signals of body sway from each local block
to represent temporal features. We acquired video sequences
containing body sway for 60 participants to evaluate genderclassiﬁcation accuracy. The gender classiﬁcation accuracy was
90.3±1.3% for our spatio-temporal feature. We conﬁrmed thatbody sway in a video sequence improves gender classiﬁcation
accuracy compared with that for parameters derived frommedical data.
In future work, we intend to develop a method for extracting
features that represent essential gender differences and are robust against posture changes. We will also investigate whether
body sway can be used to classify attributes other than gender,such as age and clothing.
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