cICIC International ⃝2023
ISSN 1881-803X

ICIC Express Letters
Volume 17, Number 11, November 2023

pp. 1217–1224

THE EFFECT OF PSEUDORANDOM PIXEL PLACEMENT
ON UNCONSTRAINED FACE RECOGNITION PERFORMANCEUSING SINGLE LEARNING IMAGE
Muhammad Sabirin Hadis1,3,∗ , Junichi Akita1 , Masashi Toda2
and Nurzaenab3
1

Department of Electrical Engineering and Computer Science
Graduate School of Natural Science and Technology
Kanazawa University
Kakuma-machi, Kanazawa, Ishikawa 920-1192, Japanakita@ifdl.jp∗
Corresponding author: muhammadsabirinhadis@ifdl.jp
2

Center for Management of Information Technologies
Kumamoto University
2-39-1 Kurokami, Chuo-ku, Kumamoto 860-8555, Japan
toda@cc.kumamoto-u.ac.jp
3

Study Program of Informatics Engineering
Universitas Teknologi AKBA Makassar
Tamalanrea Jaya 90245, Tamalanrea, Makassar, Indonesia
nurzaenab@akba.ac.id

Received January 2023; accepted April 2023
Abstract. The pseudorandom pixel placement (PSE) algorithm has been studied, which
has the eﬀect on increasing spatial sampling frequency, in order to improve the image
processing accuracy, especially for low resolution images. In this paper, we applied PSE
algorithm for the unconstrained face recognition using single learning image and evaluatedits performance for ten times to generate random patterns, compared with the conventional regular pixel placement. We used eight constrained face images for eight subjects
as training data and unconstrained face images from the converting result of the CCTV
video as testing data. We found the average accuracy is improved by 3.25% using the best
random pattern.
Keywords: Image processing, Pixel placement, Preprocessing, Single learning image,
Unconstrained face image

1. Introduction. One of the famous applications of image processing is the face recognition. The market size value for face recognition technology reaches USD 3.72 billion in
2020, and it is predicted that this value will increase to USD 11.62 billion in 2026 [1]. The
application of face recognition can be used in various ﬁelds such as security [2-5], health[6-9], and business [10,11].
In some of the face recognition applications, we have to use the low resolution images
from CCTV video camera, which results in low accuracy [12].
The authors have studied the pseudorandom pixel placement (PSE) algorithm, where
the active area in each pixel is randomly arranged, to increase eﬀective spatial sampling
frequency. PSE algorithm has the eﬀect on increasing image processing accuracy, especially for the low resolution images [13,14]. We have already demonstrated that the PSE
algorithm in several image problems to enhance the performance, such as color moire [15]and jagged edges [16,17].
DOI: 10.24507/icicel.17.11.1217

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M. S. HADIS, J. AKITA, M. TODA AND NURZAENAB

We have motived to apply the PSE algorithm for the low resolution face recognition
problem, since the PSE algorithm has the potential to increase the eﬀective spatial resolution, that is expected in enhancing the performance. In this paper, we apply the PSE
algorithm as the preprocessing step for unconstrained face recognition to emulating low
resolution camera, and evaluate its performance compared with the conventional (regular) pixel placement algorithm. We also evaluate the dependency of the performance on
random patterns.
We explain the related work in Section 2 and Section 3 explains about the pseudorandom
pixel placement with our experimental setting in this research. We present the results in
Section 4, and ﬁnally in Section 5, we conclude the results of this work.
2. Related Work. Several researchers have applied methods at the preprocessing step inorder to improve accuracy; for example, Nurzaenab et al. [14] applied the CLAHE (Contrast Limited Adaptive Histogram Equalization) method to determining the contrast limit
value in face images carried out in the preprocessing. Their work aims to reduce excessive
contrast values in face images. They made several changes to the value of the contrast
limit. The result is that their face recognition application’s accuracy has increased by
12.25%. Sharma et al. [18] implemented a resizing technique in the preprocessing stepto speed up the computational process when detecting disease in plant leaves. Ahmad
et al. [19] conducted a comparative study on the eﬀect of color conversion in the image to gray in the preprocessing step. The results show that the application performance
when detecting edges from a color to a gray image is higher than without converting. Indrabayu et al. [20] applied brightness enhancement at the preprocessing step to increasing
the accuracy of feature classiﬁcation in the eye with an average accuracy of 93.5%.
Image sensors in the camera have pixels as photo receptors, and each pixel has thepotential to arrange the position of its “active” area, which samples the incoming image, so
called the pseudorandom pixel placement. Researchers have evaluated the pseudorandom
pixel placement methods for image processing. Izaki and Akita [13] observed the structuralcharacteristics of pixels between pseudorandom and lattice pixel placement to reduce
jaggy lines in an image. The results showed that the pseudorandom pixel placement could
reduce image jaggy compared to the lattice pixel placement. Nakamura et al. [16] applied
pseudorandom pixel placement to reducing the jaggy eﬀect in moving pictures. Kobayashi
and Akita [15] conducted an experiment to solve the problem of color moire and false
color in the image. They found that the eﬀect of color moire and false color was reduced
when applying pseudorandom pixel placement. Our previous work [21] has implemented
pseudorandom pixel placement in face recognition for knowing the accuracy performancebased on face image resolutions. The results showed that the accuracy increases as the baseof face images’ resolution also increases. That work only uses one approach where each
image has a diﬀerent pattern pixel placement from the other images. Thus, in this work,
we try to apply another approach and compare the performance with PSE-R [21]. The
CMOS image sensor with the pseudorandom pixel placement has been actually designed,
fabricated, and evaluated [17].
3. Method. This section explains how to apply the PSE method in the algorithm ﬂowof the face recognition that we used. Note that this method based on the down sampling
is to emulate the low resolution camera with pixels using PSE for simulation.
3.1. Pseudorandom pixel placement (PSE). PSE can be emulated by the down
sampling with choosing the pixels by “randomly” from the original image to form a
new image structure. Here, we assume the down sampling of the original image into thehalf size. For example, in Figure 1, where the original image has a size of 4 ∗ 4 pixels(px). We handle 2 ∗ 2 px blocks for down sampling. By choosing one pixel at the same
position in each 2 ∗ 2 px block, such as the upper left, we obtain the regular (REG)ICIC EXPRESS LETTERS, VOL.17, NO.11, 2023

(a)

(b)

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(c)

Figure 1. An illustration of PSE algorithm: (a) Pixel structure in theoriginal image; (b) down sampling processing with choosing the pixels fromthe original image by pseudorandom; (c) place the selected pixel into newimage structure
down sampled image. By choosing one pixel in each 2 ∗ 2 px block randomly, we obtain
the pseudorandom pixel placement (PSE). While the pixel selections are not completely
random over the pixel plane, this method is indicated to give a good approximation forcompletely random selection [13] in terms of the spatial spectrum of sampling points.
Thus, we call this algorithm as “pseudorandom”.
We use Equation (1) in the down sampling process. The parameter pO is the pixel inthe original image (virtual pixel, or 2 ∗ 2 px block in Figure 1), and pL is the pixel in
the new image (active area). (x, y) determine the pixel position. The pixel movement forchoosing the selected pixel uses the parameters dx for the x-axis and dy for the y-axis.
The values of dx and dy is randomly determined as 0 or 1. Note that REG down samplingis represented as dx = dy = 0.
pL (x, y) = pO (2x + dx , 2y + dy )(1)

Our previous work [21] has applied the PSE method where diﬀerent random patterns
are applied to each image in all subject test, which we call PSE-R. We evaluated thatalgorithm against face image resolution by using four variations, namely, 110 ∗ 130 px,
220 ∗ 260 px, 440 ∗ 520 px, and 880 ∗ 1040 px. We obtained the result that the PSE-R
method has better accuracy with the base highest resolution at 880 ∗ 1040 px compared
to REG.
In this paper, we carried out the accuracy evaluation using diﬀerent strategies fortreating random number in order to emulate the physical implementation of CMOS image
sensor with pseudorandom pixel placement. We generate ten random patterns from PSE1
to PSE10. Each PSE-i is applied for each image of subject, from T1 to T8, and theaccuracy is calculated. We call this algorithm as PSE-S. Figure 2 shows an illustrationof the diﬀerence between PSE-R with PSE-S. We will evaluate the accuracy of PSE-S incomparison with PSE-R in [21] and REG.
We measure the accuracy, deﬁned as the ratio of the face images successfully recognizedover all the face images. The accuracy is calculated for each subject, and the averaged
accuracy is also calculated as the average of the accuracy for all the subjects.
The equation used to determine the accuracy for each face object can be seen in Equation (2) with an explanation of the term NCI for number of correct images, and NTI is1220

M. S. HADIS, J. AKITA, M. TODA AND NURZAENAB

(a)

(b)

Figure 2. The implementation model of (a) PSE-R, and (b) PSE-S
number of testing images.
Accuracy = (NCI /NTI ) × 100%

(2)

Averaged accuracy using Equation (3) where AVG means average, AT1 is the accuracy
value at T1 as the ﬁrst subject until AT8 as the last subject for the accuracy value at T8.
AVG Accuracy = (AT 1 + · · · + AT 8)/Number of Subjects(3)

3.2. Face recognition. We used the same face recognition (FC) program in our previous
work [21], where the PSE-R was applied in the pre-processing. Figure 3 shows the main
diagram ﬂow of the FC program. Figures 4 and 5 show the pre-processing step in training
and testing data. In the case of REG, we omit the PSE step.

Figure 3. The main steps ﬂow in face recognition

Figure 4. The steps ﬂow in pre-processing for training data

Figure 5. The steps ﬂow in pre-processing for testing data
We use single learning image per person (SLIPP) for training data. There are 8 subjects
for test, we called T1 for the ﬁrst subject, T2 for the second subject to T8 for the last
subject, as shown in Figure 6. In the testing data, we use unconstrained faces from theconverting result of the CCTV Video, as shown in Figure 7. We use the biggest resolution
at 880 ∗ 1040 px for the testing data because that resolution achieves the highest average
accuracy when applying PSE-R [21]. The details of training and testing data speciﬁcation
can be seen in Table 1.

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Figure 6. The training data based on SLIPP concept for 8 subjects where
T1 is in the far left to T8 in the far right

Figure 7. The testing data using unconstrained face images from CCTV Video
Table 1. Training and testing data speciﬁcation
Subject
T1
T2
T3
T4
T5
T6
T7
T8

Training
AmountResolution
(pcs)
(px)
1
1023 ∗ 1441
1
888 ∗ 1193
1
1012 ∗ 1338
1
1078 ∗ 1575
11023 ∗ 1441
1
2019 ∗ 3192
12869 ∗ 4227
11758 ∗ 2900

TestingAmountResolution
(pcs)
(px)
351
381
406
360
880 ∗ 1040
373
248
254
299

FPS
CCTV
Video

30

15

The term of pcs is pieces, px is pixel, and FPS is frame per second.

4. Result and Discussion. The accuracy performance in each PSE-S variation, as wellas REG and results from PSE-R [21] are shown in Figure 8. We found the accuracy is
diﬀerent for the variations of random patterns; however, most PSE-S gives better accuracy
than REG.
We got the most signiﬁcant improvement in accuracy when applying PSE-S in T6
at PSE8 by 22.98% compared to REG and by 12.50% compared to PSE-R [21]. Thatcase also becomes the lowest performance on REG. For the lowest accuracy, we found in
T5 at PSE3 with a decrease by 9.38% and 16.09% compared to REG and PSE-R [21],respectively.

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M. S. HADIS, J. AKITA, M. TODA AND NURZAENAB

Figure 8. The accuracy for all subjects over the pixel placement patterns
In the case of REG, the best accuracy is on T3. Compared to the highest PSE-S at
PSE1 in the same subject, the PSE-S is better with a diﬀerence by 1.72%. In case of thelowest PSE-S at PSE2, the PSE-S accuracy decreases by 0.49% while compared to PSE-R[21] increasing at same value by 0.49%. The highest accuracy in PSE-R [21] obtains in
T5. That value is higher by 6.70% compared to REG and 0.80% lower compared to thehighest PSE9 at the same subject. The big diﬀerence shows up when we compare REG
to the highest PSE-S at PSE8 for T6, the PSE-S accuracy increasing by 22.98%.
In the accuracy for each subject based on the overall experiment, we can see a signiﬁcantdiﬀerence between two groups of subjects, group 1 with members T1 to T5 and group
2 with members T6 to T8. In the case of REG, Group 1 has an average accuracy of
81.10%, while Group 2 has 27.24%. The diﬀerence is 53.86%. The cause is the diﬀerencesin the training and testing data used and the diﬀerences between devices used to takeface photos, also shooting angles.
The CCTV Video shooting angle in Group 2 is at a slope of ±15◦ , while Group 1
is straight to the subject. The training data for both groups of subjects have the samemodel, the face facing perpendicular to the front. The diﬀerence in the angle of taking
testing data in Group 2 is one of the reasons why the accuracy value of Group 2 is lower
than Group 1. However, after applying the PSE-S, we can increase the average accuracy
in Group 1 by 1.27% with a total value 82.37% and Group 2 by 6.55% with a total value
33.79% based on the best pattern at PSE2.
In the case of the average accuracy, we got the results for the highest obtained in PSE2
with 64.15%. The performance increased by 3.25% against REG and 0.39% compared
to PSE-R [21]. In this work, with ten variations of PSE-S pattern in all face image, thedecrease in accuracy is no more than 0.51% for REG and 3.37% compared to PSE-R [21].
The graph of average accuracy performance can be seen in Figure 9.
Based on the best average PSE performance in PSE2, we can see that this method canprovide a lot of improvement in Group 2, which includes 3 subjects, namely T6, T7, andT8. These subjects have a test image with lower resolution, and the testing data collection
environment is more challenging than Group 1. The placement of patterns inﬂuences
the feature classiﬁcation process to determine whether an image can be recognized. The
performance diﬀerence obtained was 5.28% higher than Group 1.

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Figure 9. The averaged accuracy for pixel placement patterns
5. Conclusions. We conﬁrmed that the accuracy of our proposed algorithm (PSE-S)
depends on the pattern variation, as in the case of PSE-R. The highest performance of
PSE-S was achieved on T6 at PSE8, with accuracy increasing by 22.98% compared to the
REG and 12.50% compared to PSE-R [21]. We got the lowest performance of PSE-S on
T5 at PSE3, with accuracy decreased by 9.38% compared to REG and 16.09% compared
to PSE-R [21]. From the random patterns in this simulation, the PSE-S has better performance accuracy in all subjects. In the case of average accuracy, PSE-S can increase facerecognition performance by 3.25% against REG and 0.39% compared to PSE-R [21] at thehighest value obtained in PSE2. This result indicates that our proposed algorithm, whichemulates the actual CMOS image sensor with pseudorandom pixel placement, performs
better than REG. In the future research, we want to evaluate the PSE-S performance inanother face recognition framework model that uses more subjects.
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