Feature Extraction using Downsampling for Person Re-identiﬁcation

with Low-resolution Images

Masashi Nishiyama

, Takuya Endo and Yoshio Iwai

Graduate School of Engineering, Tottori University, Tottori, Japan

Keywords:

Person Re-identiﬁcation, Low-resolution Images, Low-frequency Components, Downsampling.

Abstract:

We investigate whether a downsampling process of high-resolution pedestrian images can improve person re-

identiﬁcation accuracy. Generally, deep-learning and machine-learning techniques are used to extract features

that are unaffected by image resolution. However, it requires a large number of pairs of high- and low-

resolution images acquired from the same person. Here, we consider a situation in which these resolution

pairs cannot be collected. We extract features from low-resolution pedestrian images using only a simple

downsampling process that requires no training resolution pairs. We collected image resolution datasets by

changing the focal length of the camera lens and the distance from the person to the camera. We conﬁrmed that

the person re-identiﬁcation accuracy of the downsampling process was superior to that of the upsampling. We

also conﬁrmed that the low-frequency components corresponding to the output of the downsampling process

contain many discriminative features.

1 INTRODUCTION

There is a need for a person re-identiﬁcation sys-

tem that covers wide areas using surveillance cam-

eras installed at various locations to track people.

This system is expected to search for the routes of

lost children and suspicious persons to make society

safe and secure. The key task in designing a per-

son re-identiﬁcation system is to determine how to

extract the features that represent the personal char-

acteristics from pedestrian images. To do this, var-

ious existing methods (Zhong et al., 2017; Zheng

et al., 2015) have been proposed. When extracting

features, existing methods perform better if they ac-

quire high-resolution (HR) pedestrian images con-

taining many personal characteristics. In general, HR

images increase the probability of successful identi-

ﬁcation. However, the resolution of pedestrian im-

ages obtained from surveillance cameras is not always

high; it varies dynamically depending on the camera

settings, such as the camera lens and camera position.

When pedestrian images are acquired at low resolu-

tion, it becomes difﬁcult to stably extract features that

correctly represent the characteristics of the individu-

als. This causes the person re-identiﬁcation accuracy

to decrease.

https://orcid.org/0000-0002-5964-3209

https://orcid.org/0000-0003-2248-4563

To overcome the problem caused by low-

resolution (LR) pedestrian images, existing meth-

ods (Jing et al., 2015; Wang et al., 2016; Zheng

et al., 2018; Jiao et al., 2018) take into account res-

olution variation at the feature extraction process for

person re-identiﬁcation. These existing methods use

a learning-based approach in which pairs of HR and

LR images of the same person are used as train-

ing samples for deep-learning and machine-learning

techniques. They promise to improve person re-

identiﬁcation accuracy by extracting features that are

less affected by the resolution variation. However,

the learning-based approach frequently requires the

preparation of a large number of HR and LR image

pairs, and their collection is very labor-intensive.

Our challenge is to determine whether the accu-

racy of person re-identiﬁcation can be improved when

there are no HR and LR training image pairs of the

same individual. Instead of a learning-based approach

that requires a large number of training samples, we

exploit a resampling approach. Our approach has the

advantage that it can be applied in cases where train-

ing samples consisting of pairs of HR and LR images

cannot be collected. There are two types of resam-

pling processes: upsampling to match the size of the

HR target images and downsampling to match the size

of the LR query images. We experimentally demon-

strated which process is more suitable for improv-

Nishiyama, M., Endo, T. and Iwai, Y.

Feature Extraction using Downsampling for Person Re-identiﬁcation with Low-resolution Images.

DOI: 10.5220/0010815500003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP, pages

351-358

ISBN: 978-989-758-555-5; ISSN: 2184-4321

351

ing person re-identiﬁcation accuracy. We conﬁrmed

that the downsampling process obtained signiﬁcantly

higher person re-identiﬁcation accuracy than the up-

sampling process. We also conﬁrmed that the low-

frequency components corresponding to the output of

the downsampling process contain informative fea-

tures on the publicly available CUHK01 dataset.

2 RELATED WORK

We ﬁrst review a survey paper (Wang et al., 2014) fo-

cusing on image resolution in the ﬁeld of face recog-

nition, where the purpose is relevant to our paper. The

survey paper states that two approaches exist to deal

with face recognition problems caused by low resolu-

tion. In the ﬁrst approach (Shi and Jain, 2019), a fea-

ture space that is less affected by resolution variation

is designed, and the recognition process is performed

in that feature space. In the second approach (Zou and

Yuen, 2011), LR images are converted to HR images,

and the recognition process is performed in an HR

feature space. In especial, some researchers (Agh-

dam et al., 2019; Hennings-Yeomans et al., 2008) dis-

cussed the suitable resolutions for face recognition.

However, these related studies did not treat the case

of person re-identiﬁcation. Recently, several existing

methods employing the ﬁrst and second approaches

have also been proposed for person re-identiﬁcation.

Using the ﬁrst approach, the existing meth-

ods (Jing et al., 2015; Wang et al., 2016) have been

proposed to design a feature space that is less sensitive

to resolution variation for person re-identiﬁcation.

They exploit learning-based methods to design fea-

ture spaces that are not easily affected by resolution

variations using deep-learning or machine-learning

techniques with pairs of HR and LR images of the

same person. Using the second approach for per-

son re-identiﬁcation, the existing methods (Zheng

et al., 2018; Jiao et al., 2018) have been proposed.

To convert LR images to HR images, they exploit

super-resolution techniques, which incorporate deep-

learning models trained from the same person’s reso-

lution image pairs.

In both approaches, if we prepare a large num-

ber of training samples of resolution image pairs,

we can expect to increase the accuracy of the per-

son re-identiﬁcation substantially. However, collect-

ing a large number of training samples of resolution

image pairs is very laborious and costly. In this pa-

per, we aim to improve the accuracy using a resam-

pling approach in which only preprocessing is applied

to pedestrian images instead of a learning-based ap-

proach in which training samples are required.

3 EFFECT OF IMAGE

DOWNSAMPLING

3.1 Image Resampling Methods

The resolution of pedestrian images in person re-

identiﬁcation systems varies in both the query sam-

ples and target samples. To simplify the problem

setup, we assume that HR pedestrian images are given

to target samples and LR pedestrian images are given

to query samples. Under this assumption, we eval-

uate whether the pedestrians in the query and target

samples are identical or not. We compare person

re-identiﬁcation accuracy using the following two re-

sampling processes:

• C

: We perform downsampling on the HR target

images to match the size of the LR query images.

Then, we identify the original LR query images

using the downsampled HR target images.

• C

: We perform upsampling on the LR query im-

ages to match the size of the HR target images.

Then, we identify the upsampled LR query im-

ages using the original HR target images.

We use bilinear interpolation for C

and a super-

resolution convolutional neural network (SR-

CNN) (Dong et al., 2015) for C

3.2 Factors Reducing the Resolution of

Pedestrian Images

To evaluate the person re-identiﬁcation accuracy us-

ing the resampling process, it is necessary to collect

a dataset of pedestrian images consisting of query

and target samples. To determine a dataset collection

strategy, we considered the factors that cause a de-

crease in resolution in various camera settings. Typ-

ical factors are represented by the parameter changes

in the camera lens and camera positions. Each factor

is described in detail below.

We consider the inﬂuence of the camera lens on

the resolution. The performance of camera sensors

has improved remarkably in recent decades, and the

majority of camera sensors have a large number of

pixels. Thus, images acquired from cameras usually

have high resolution. However, a single surveillance

camera covers a wide ﬁeld of view, and the resolution

of an image of an individual pedestrian is often low.

Furthermore, the resolution of the pedestrian image

decreases when the focal length of the camera lens

is small, even if the number of pixels of the camera

sensor is large. For example, the resolution becomes

low when a wide-angle lens with a short focal length

is attached.

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352

Next, we consider the inﬂuence of the camera po-

sition on resolution. The position of a camera varies

depending on the purpose of the surveillance systems.

For example, to monitor a speciﬁc point, such as the

door to a train, the cameras are placed close to pas-

sengers who are entering the train. To monitor a large

space, such as an airport lobby, the cameras are placed

so that they have a wide view that is far away from

people. Assuming that the camera lenses are identi-

cal, the resolution is high when the distance between

the camera and the person is small. In contrast, the

resolution is low when this distance is large.

In the following, we describe two sets for evalu-

ating person re-identiﬁcation accuracy: one in which

the focal length of the camera lens changes and an-

other in which the distance between the person and

camera changes.

3.3 Data Collection of LR Pedestrian

Images

3.3.1 Focal Length Change Dataset

Figure 1 shows the camera setting used when the

focal length of the camera lens was changed. We

set the focal length to 135 mm, 70 mm, 35 mm,

and 18 mm and acquired query pedestrian images for

each setting. The average sizes of the pedestrian im-

ages were (58.3, 186.3), (31.3, 100.5), (15.4, 49.5),

and (7.2, 24.2) pixels, respectively. We used a fo-

cal length of 135 mm, to acquire the target pedes-

trian images at a different time. The camera (Sony

α6300 with E 18-135mm F3.5-5.6 OSS) was placed

at a height of 167.3 cm above the ﬂoor. The distance

from the person to the camera was ﬁxed at 15 m. The

images were acquired at ﬁve different indoor loca-

tions unaffected by ambient light. Twenty subjects

(17 men and three women) participated.

3.3.2 Distance Change Dataset

Figure 2 shows the camera setting used when the dis-

tance from the camera to the person was changed.

We set the distance to 5 m, 10 m, 15 m, 20 m, and

25 m and again acquired the query pedestrian images

for each setting. The average sizes of the pedestrian

images were (59.2, 192.9), (29.2, 94.7), (19.6, 62.4),

(14.3, 46.7), and (11.7, 37.1) pixels, respectively. We

used a distance of 5 m to acquire the target pedestrian

images at a different time. The camera (Logicool HD

Pro Webcam C920r) was placed at a height of 167.3

cm above the ﬂoor. To keep the lighting on the per-

son constant, we ﬁxed the person’s standing position

and moved the camera position. The images were ac-

167.3 cm

135 mm – 18 mm

15 m

135 mm 70 mm 18 mm35 mm

Location

135 mm 70 mm 18 mm35 mm

Focal length:

Height:

Distance:

Location

Person 1

Person 2

Focal length

Figure 1: Camera setting when the focal length of the cam-

era lens is changed.

quired at ﬁve different indoor locations. Twenty sub-

jects (17 men and three women) participated.

3.4 Accuracy of Person

Re-identiﬁcation

We performed the downsampling C

and upsampling

described in Section 3.1 to pedestrian images

as preprocessing before feature extraction. We ex-

tracted features using co-occurrence attributes pro-

posed by (Nishiyama et al., 2016). Co-occurrence

attributes are represented by combinations of phys-

ical and adhered human characteristics (e.g., a man

wearing a suit, a 20-something woman, or a woman

with long hair who is wearing a skirt). To increase the

accuracy, we applied the large margin nearest neigh-

bor (LMNN) (Weinberger and Saul, 2009), which is

a kind of metric learning technique. We used the

CUHK01 dataset (Li et al., 2012) as training sam-

ples only for the metric learning. We applied the

Feature Extraction using Downsampling for Person Re-identiﬁcation with Low-resolution Images

353

167.3 cm

5 m 10 m 25 m20 m15 m0 m

5 m 10 m 25 m20 m15 m

Location

5 m 10 m 25 m20 m15 m

Height:

Distance:

Person 1

Person 2

Location

Distance

Figure 2: Camera setting when the camera–person distance

is changed.

nearest neighbor approach using Euclidean distance

to compute distances between the query and target

features. We used the ﬁrst matching rate (Rank-1),

which measures a correct hit between the person in

the query sample and one of the target samples. We

computed the average and the standard deviation of

ﬁrst matching rate from the ﬁve locations included in

each dataset.

We evaluated the person re-identiﬁcation accuracy

on the dataset with varying camera lens focal lengths.

Figure 3 shows the average and the standard devia-

tion of ﬁrst matching rate of person re-identiﬁcation

when C

and C

were applied in the focal length

dataset. We see that C

, which downsamples to the

size of the LR query samples, improves the person

re-identiﬁcation accuracy better than C

, which up-

samples to the size of the HR target samples.

Next, we evaluated the person re-identiﬁcation ac-

curacy on the dataset with varying distances from the

person to the camera. The experimental conditions

were the same as those in the above evaluation ex-

cept for the dataset. Figure 4 shows the average and

135

Focal length of the camera lens (mm)

100

Accuracy (%)

𝑑

(Downsampling)

𝑢

(Upsampling)

Figure 3: Accuracy of person re-identiﬁcation when chang-

ing the focal length of the camera lens.

100

10 15 20 255

Distance from the camera to the person (m)

Accuracy (%)

𝑑

(Downsampling)

𝑢

(Upsampling)

Figure 4: Accuracy of person re-identiﬁcation when chang-

ing the camera–person distance.

the standard deviation of ﬁrst matching rate of person

re-identiﬁcation when C

and C

were applied in the

distance change dataset. Similar to the above results,

we see that downsampling C

obtains better accuracy

than C

We believe that downsampling C

is more suit-

able as a preprocessing technique for person re-

identiﬁcation than upsampling C

in both the datasets,

where the resolution is reduced by the focal length of

the camera lens and the distance from the camera to

the person.

4 ANALYSIS OF

DOWNSAMPLING ON PERSON

RE-IDENTIFICATION

4.1 Overview

The downsampling process improves the person re-

identiﬁcation accuracy more than the upsampling pro-

cess, as described in Section 3.4. Here, we use

spatial frequency analysis to further investigate why

the downsampling process increases the accuracy.

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

354

𝒇

𝒈

"↑

𝒈

"↑

SRCNN

Original

images

𝒇

𝒈

"↑

Figure 5: Visualization of pedestrian images consisting

of high-frequency and low-frequency components on the

CUHK01 dataset.

We assume that an HR image can be separated into

low-frequency components and high-frequency com-

ponents. In particular, we consider that the low-

frequency components of an HR image correspond

to an LR image. In this experiment, we investigate

how person re-identiﬁcation accuracy varies when us-

ing the following three images:

: Images containing all frequency components.

: Images containing low-frequency components.

: Images containing high-frequency components.

We use pseudo HR images upsampled from LR im-

ages with the SRCNN as the query samples. The

target samples are the original HR images. We con-

sider that F

corresponds to C

with upsampling in

Section 3.1, and F

corresponds to C

with downsam-

pling.

4.2 Experimental Conditions

We performed image degradation simulations using

a model where HR pedestrian images were used to

create various LR images. This model synthesized the

LR query samples g

using the following equation:

= BH f

+ n

n (1)

where B is the downsampling process, H is the blur

process, f

is the original HR query samples, and n

n is

noise. We assumed that B and H are shift-invariant.

Speciﬁcally, B represents bilinear interpolation, H

Gaussian blur, and n

n white Gaussian noise.

We used the CUHK01 dataset (Li et al., 2012),

consisting of 971 pedestrians. We assigned pedes-

trian images acquired at different times to the query

and target. We evaluated 971 query samples and 971

target samples. The size of all pedestrian images was

normalized to (60, 160) pixels. To generate the LR

query samples g

using Eq. (1), we set the image sizes

to (30, 80), (24, 64), (18, 48), (12, 32), and (6, 16)

pixels. We also generated upsampled LR query sam-

ples g

q↑

with a size of (60, 160) pixels from g

us-

ing the SRCNN. The size of the target samples was

(60, 160) pixels.

We separated g

q↑

into high-frequency and low-

frequency components in the frequency domain using

a discrete Fourier transform. Then, each component

was transformed into an image in the spatial domain

using an inverse discrete Fourier transform. We set

the boundary between the low-frequency and high-

frequency components to match the resolution of each

pedestrian image. We created images g

q↑

containing

only high-frequency components computed from g

q↑

and images g

q↑

containing only low-frequency com-

ponents. We also created images f

containing only

high-frequency components of the original HR im-

age f

and images f

containing only low-frequency

components.

Figure 5 shows a visualization of the high-

frequency and low-frequency component images. We

used a size of f

and g

q↑

with (60, 160) pixels. Image

q↑

was upsampled from g

with (6, 16) pixels. We

mapped the range of pixel values from [−128, 128] to

[0, 255] to visualize high-frequency components be-

cause the high-frequency components can take posi-

tive or negative values. When we focus on the high-

frequency components, we see that the edges of the

face and the pattern of the clothing occur in the high-

frequency components f

of the original images. In

contrast, some weak edges occur around the pedes-

trian’s contour in the high-frequency images g

q↑

up-

sampled using the SRCNN. We also see that the ap-

pearances of the pedestrians are similar in the low-

frequency image f

computed from the original im-

age and the low-frequency image g

q↑

computed from

q↑

upsampled using the SRCNN.

4.3 Accuracy of Person

Re-identiﬁcation on the CUHK01

Dataset

To perform person re-identiﬁcation, we used the fol-

lowing methods:

• CA: LMNN with the co-occurrence attributes de-

scribed in (Nishiyama et al., 2016) (We explained

the CA method in Section 3.4).

• SL: CNN with softmax cross entropy loss with

label smoothing regularizer described in (Szegedy

Feature Extraction using Downsampling for Person Re-identiﬁcation with Low-resolution Images

355

et al., 2016; Szegedy et al., 2016).

• TL: CNN with triplet loss described in (Hermans

et al., 2017).

• OS: Omni-Scale Network (OSNet) described

in (Zhou et al., 2019).

We used 486 randomly selected pedestrians for train-

ing the LMNN metric matrix of the CA method from

the CUHK01 dataset. The remaining 485 pedestrians

in this dataset were used for the query and target sam-

ples of the CA method. We used the same pedestrian

images employed in the CA method for the query and

target samples of the the SL, TL, and OS methods.

The backbone network of the SL and TL methods was

ResNet50 (He et al., 2016). We used the Torchreid

implementation (Zhou and Xiang, 2019) with train-

ing samples of the Market-1501 dataset (Zheng et al.,

2015) for the SL, TL, and OS methods.

Table 1 shows the person re-identiﬁcation accu-

racy using the CA, SL, TL, and OS methods on the

CUHK01 dataset. We see that F

with low-frequency

component images g

q↑

is superior to F

with g

q↑

and

with g

q↑

for all the methods. In the case of F

with

only high-frequency component images, no accuracy

could be obtained at all. Figure 6 shows the receiver

operating characteristic (ROC) curves when using the

, F

, and F

conditions with the CA and OS meth-

ods. We used g

q↑

upsampled from g

with (18, 48)

pixels. We see that F

is superior to F

and F

accord-

ing to the ROC curves for both the CA and OS meth-

ods. We believe that the low-frequency components

corresponding to the output of the downsampling pro-

cess yields promising results regardless of the design

of the person re-identiﬁcation method.

Furthermore, we also checked the performance of

the downsampling C

with bilinear interpolation and

upsampling C

with SRCNN, as described in Sec-

tion 3.1. We used g

in C

and g

q↑

in C

. In the

case of the CA method, the accuracy of C

were

76.2% with (30, 80) pixels, 75.1% with (24, 64) pix-

els, 74.5% with (18, 48) pixels, 70.4% with (12, 32)

pixels, and 56.6% with (6, 16) pixels. Those of C

were 70.9%, 70.4%, 11.9%, 2.6%, and 0.5%, re-

spectively. Next, in the case of the OS method,

the accuracy of C

were 93.5% with (30, 80) pix-

els, 89.7% with (24, 64) pixels, 72.6% with (18, 48)

pixels, 34.0% with (12, 32) pixels, and 25.7% with

(6, 16) pixels. Those of C

were 78.7%, 76.8%,

69.7%, 52.3%, and 19.0%, respectively. We con-

ﬁrmed that downsampling C

obtains signiﬁcantly

better accuracy than upsampling C

on the CUHK01

dataset. We believe the effectiveness of downsam-

pling appears in this experiment as well as in the re-

sults in Section 3.4.

True positive rate

False positive rate

━ F

(AUC = 0.978)

━ F

(AUC = 0.991)

━ F

(AUC = 0.513)

(b) OS

True positive rate

False positive rate

━

(AUC = 0.843)

━

(AUC = 0.982)

━

(AUC = 0.507)

(a) CA

True positive rate

False positive rate

━ F

(AUC = 0.978)

━ F

(AUC = 0.991)

━ F

(AUC = 0.513)

(b) OS

True positive rate

False positive rate

━

(AUC = 0.843)

━

(AUC = 0.982)

━

(AUC = 0.507)

(a) CA

Figure 6: ROC curves when using the F

, F

, and F

condi-

tions on the CUHK01 dataset. We checked the area under

the curve (AUC).

4.4 Evaluation of Different

Super-resolution Techniques

We evaluated the person re-identiﬁcation accu-

racy using existing super-resolution techniques SR-

CNN (Dong et al., 2015), ESRGAN (Wang et al.,

2018), and USRNet (Zhang et al., 2020) as upsam-

pling processes. We also evaluated it using basic bi-

linear and bicubic interpolation techniques. The ex-

perimental conditions are the same as those described

in Section 4.3 except for the upsampling techniques.

To perform person re-identiﬁcation, we used the OS

method. Table 2 shows the person re-identiﬁcation

accuracy when using the bilinear, bicubic, SRCNN,

ESRGAN, and USRNet techniques. We see that

obtained a higher accuracy than F

and F

re-

gardless of which image interpolation technique was

used. We believe that informative features for person

re-identiﬁcation are contained in the low-frequency

components. Since F

provides the same effect as

, we again conﬁrmed the downsampling process

obtains better accuracy than the upsampling process,

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

356

Table 1: Comparison of the accuracy of the F

, F

, and F

methods when using the person re-identiﬁcation methods CA, SL,

TL, and OS on the CUHK01 dataset.

Image size Feature Accuracy (%)

of g

with g

q↑

with g

q↑

with g

q↑

(30, 80)

CA 70.9 ± 1.7 73.3 ± 0.4 0.2 ± 0.0

SL 62.8 ± 1.0 65.2 ± 0.7 0.2 ± 0.0

TL 49.6 ± 1.9 52.3 ± 1.4 0.3± 0.1

OS 78.8 ± 1.0 88.2 ± 0.4 0.2 ± 0.1

(24, 64)

CA 70.4 ± 1.2 73.7 ± 0.3 0.2 ± 0.0

SL 61.4 ± 0.7 64.3 ± 1.9 0.1 ± 0.1

TL 48.0 ± 2.6 54.1 ± 1.6 0.2 ± 0.0

OS 77.4 ± 1.5 87.7 ± 0.3 0.2 ± 0.0

(18, 48)

CA 11.9 ± 1.2 74.0 ± 0.6 0.2 ± 0.0

SL 53.7 ± 0.5 56.3 ± 0.9 0.4 ± 0.1

TL 44.1 ± 1.9 57.1 ± 0.8 0.2 ± 0.0

OS 71.3 ± 2.1 83.3 ± 0.6 0.3 ± 0.0

(12, 32)

CA 2.6 ± 0.4 74.0 ± 0.7 0.2 ± 0.0

SL 41.0 ± 0.7 50.9 ± 1.9 0.2 ± 0.0

TL 37.2 ± 1.5 60.3 ± 1.0 0.2 ± 0.0

OS 53.1 ± 0.5 70.8 ± 0.9 0.2 ± 0.0

(6, 16)

CA 0.6 ± 0.2 71.1 ± 0.6 0.2 ± 0.0

SL 17.5 ± 0.4 52.6 ± 1.3 0.4 ± 0.2

TL 26.8 ± 1.1 64.1 ± 0.8 0.3 ± 0.1

OS 19.3 ± 0.7 60.6 ± 1.5 0.3 ± 0.1

Table 2: Comparison of the accuracy of the F

, F

, and F

conditions using super-resolution techniques (SRCNN, ESRGAN,

and USRNet) and basic interpolation techniques (Bilinear and Bicubic) on the CUHK01 dataset.

Image size Interpolation Accuracy (%)

of g

technique F

with g

q↑

with g

q↑

with g

q↑

(30, 80)

Bilinear 76.8 ± 1.6 86.0 ± 0.9 0.2 ± 0.0

Bicubic 75.8 ± 1.6 85.6 ± 1.5 0.3 ± 0.1

SRCNN 78.8 ± 1.0 88.2 ± 0.4 0.2 ± 0.1

ESRGAN 78.1 ± 1.2 88.5 ± 0.6 0.4± 0.0

USRNet 77.5 ± 1.5 86.8 ± 1.3 0.3 ± 0.1

(24, 64)

Bilinear 70.4 ± 2.3 80.5 ± 0.8 0.2 ± 0.0

Bicubic 69.8 ± 2.6 82.4 ± 0.4 0.3 ± 0.1

SRCNN 77.4 ± 1.5 87.7 ± 0.3 0.2 ± 0.0

ESRGAN 75.9 ± 0.8 88.0 ± 0.3 0.1 ± 0.1

USRNet 73.7 ± 2.3 84.1 ± 0.0 0.3 ± 0.0

(18, 48)

Bilinear 53.2 ± 2.5 61.7 ± 1.2 0.2 ± 0.0

Bicubic 55.8 ± 1.6 69.1 ± 1.1 0.2 ± 0.0

SRCNN 71.3 ± 2.1 83.3 ± 0.6 0.3 ± 0.0

ESRGAN 69.3 ± 0.4 85.9 ± 0.7 0.5 ± 0.1

USRNet 59.4 ± 1.1 71.9 ± 0.1 0.2 ± 0.0

(12, 32)

Bilinear 29.4 ± 1.6 33.1 ± 1.5 0.2 ± 0.0

Bicubic 31.0 ± 1.6 42.3 ± 1.7 0.2 ± 0.0

SRCNN 53.1 ± 0.5 70.8 ± 0.9 0.2 ± 0.0

ESRGAN 62.7 ± 1.1 81.1± 0.2 1.3 ± 0.2

USRNet 32.5 ± 0.2 45.5 ± 1.0 0.2 ± 0.0

(6, 16)

Bilinear 7.6 ± 0.1 18.2 ± 0.5 0.2 ± 0.0

Bicubic 8.1 ± 1.2 27.8 ± 0.3 0.2 ± 0.0

SRCNN 19.3 ± 0.7 60.6 ± 1.5 0.3 ± 0.1

ESRGAN 36.8 ± 1.8 57.6 ± 1.2 1.3 ± 0.2

USRNet 7.8 ± 0.7 30.8 ± 1.4 0.3 ± 0.1

even when using super-resolution techniques and ba-

sic interpolation techniques.

5 CONCLUSIONS

We investigated whether the downsampling process,

which does not require training samples of HR and

LR image pairs, obtains better person re-identiﬁcation

accuracy than upsampling. We collected datasets in

two camera settings to evaluate the inﬂuence of res-

olution variations by changing the focal length of the

camera lens and the distance from the camera to the

person. The experimental results show that down-

sampling to match the size of LR images contributes

signiﬁcantly to obtaining high person re-identiﬁcation

Feature Extraction using Downsampling for Person Re-identiﬁcation with Low-resolution Images

357

accuracy, in contrast to upsampling to match the

size of HR images. We also compared the accuracy

when using high-frequency and low-frequency com-

ponents extracted from the upsampled LR images in

simulation experiments. We conﬁrmed that the low-

frequency components, which correspond to down-

sampled images, are likely to contain many informa-

tive features.

In future work, we intend to develop a re-

sampling method for further increasing person re-

identiﬁcation accuracy and expand the evaluation

on various datasets containing person appearances

changes. We also need to analyze the relevance of

our ﬁndings to the image generation process, such as

optical systems in real environments.

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