An effective facial expression recognition approach for intelligent game systems

Int. J. Computational Vision and Robotics, Vol. 6, No. 3, 2016

An effective facial expression recognition approach
for intelligent game systems
Nhan Thi Cao
School of Media,
Soongsil University,
511, Sangdo-Dong, Dongjak-Gu,
Seoul, 156-743, Korea
Email:

An Hoa Ton-That
University of Information Technology,
Vietnam National University,
Km 20, Hanoi Highway, Linh Trung Ward,
Thu Duc District, Ho Chi Minh City, Vietnam
Email:

Hyung-Il Choi*
School of Media,
Soongsil University,
511, Sangdo-Dong, Dongjak-Gu,
Seoul, 156-743, Korea
Email:
*Corresponding author
Abstract: This paper presents a novel facial expression recognition approach
based on an improved model of completed local binary pattern and support
vector machine classification to propose a method for applying to intelligence
game applications as well as intelligence communication systems. The
capturing emotion of players can be applied in interactive games with various
purposes, such as transferring player’s emotions to his or her avatar, or

activating suitable action to communicate with players in order to obtain
positive attitude of the players in educational games. Our experiments on two
databases included JAFFE (213 images) and CK (2040 images) databases show
the effectiveness of the proposed method in comparison with some other
methods. The accuracy recognition rate of JAFFE database is 96.28% and CK
database is 99.85%. The advantage of this technique is simple, fast and high
accuracy.
Keywords: facial expression recognition; completed local binary pattern;
CLBP; intelligence game systems; support vector machine; SVM.
Reference to this paper should be made as follows: Cao, N.T., Ton-That, A.H.
and Choi, H-I. (2016) ‘An effective facial expression recognition approach for
intelligent game systems’, Int. J. Computational Vision and Robotics, Vol. 6,
No. 3, pp.223–234.
Copyright © 2016 Inderscience Enterprises Ltd.

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Biographical notes: Nhan Thi Cao is a PhD candidate at Computer Vision Lab
in the School of Media at Soongsil University. She received her BS (1998) in
Information Technology from Dalat University. She received her MS (2004) in
Computer Science from University of Natural Science, Vietnam National
University, Ho Chi Minh City.
An Hoa Ton-That holds a PhD in Computer Science Department at the
University of Information Technology, which belongs to Vietnam National
University, Ho Chi Minh City, Vietnam. His research interests include
computer vision, pattern recognition, fuzzy systems and artificial intelligence.

He received his BS (2005) in Information Technology. He received his MS
(2009) in Computer Science from Vietnam National University, Ho Chi Minh
City, Vietnam. He received his PhD (2014) in Computer Science from Soongsil
University, Korea.
Hyung-Il Choi is a Professor in the School of Media at Soongsil University. His
research interests include computer vision, pattern recognition, and artificial
intelligence. He received his BS (1979) in Electronic Engineering from Yonsei
University. He received his MS (1983) and PhD (1987) degrees in Electrical
Engineering and Computer Science from the University of Michigan.
This paper is a revised and expanded version of a paper entitled ‘A facial
expression recognition method for intelligent game applications’ presented at
Serious Games & Social Connect Community Conference and the International
Symposium on Simulation & Serious Games 2014, Kintex Convention Center,
South Korea, 23–24 May 2014.

1

Introduction

In recent years, with the development of intelligence communication systems, data-driven
animation and intelligent game applications, facial expression recognition has attracted
much attention as in Ahsan et al. (2013), Cao et al. (2013), Liao et al. (2006), Priya and
Banu (2012), Shan et al. (2005, 2009), Zhao and Zhang (2012), for example. In this
paper, we propose a novel method for recognising facial expressions based on an
improvement of a completed modelling of local binary pattern. Our experiments on both
Japanese Female Facial Expression (JAFFE) database as in Lyons et al. (1999) and
Cohn-Kanade (CK) database as in Kanade et al. (2000) and Lucey et al. (2010) show the
effectiveness of the proposed method. The accuracy rate obtained is high in compare with
several other methods for both databases with seven classes of facial expression.
In intelligent games, emotion recognition of players through facial expression

recognition can be used in many ways. For example, in interactive and multiplayer
games, emotions of players can be transferred to the players’ avatars on the screen. Or in
educational games, recognising players’ emotions can help the system how to behave in
better manner. For example, if the player is sleepy, the system may wake him/her up; or
if the player is happy after doing something well, the system may cheer him/her up and
so on. Thus, facial expressions recognition of players is applied, intelligent game systems
can become more interactive, vivid and attractive.


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The rest of the paper is organised as follows: in Section 2, the face region cropping is
described. Section 3 presents the completed local binary pattern (CLBP) for facial
expression recognition and in Section 4, experiments and results are shown. Finally, in
Section 5, the conclusions are given.

2

Face region cropping

Face image pre-process is a process to attain normalised face images from input face
images gotten from a camera or a database. The normalised face images are used for
extracting facial expression features. This process can be divided into two steps: basic
step and enhancement step. The basic step is to detect the face region of an input face
image and eliminate redundant regions. This step can carry out by manual or a real-time
face detector. The enhancement step is to optimise the face region for extracting facial
expression features. This step can be made by cropping methods, image normalisation or
image filter processes. Then the face images are rescaled and used for feature extraction.

Figure 1 shows the process of face image preprocess.
Figure 1

The process of face image preprocess

Database/
camera

Input face
images

Basic
processing

Enhancement
processing

Feature
extraction

In this paper, the image preprocess is implemented as in Cao et al. (2013). It included two
steps of preprocess: basic process and enhancement process. Normally, human face
images from a camera or a database contain much redundant information, e.g.,
background or non-face regions. So, to detect face region in face image, the robust
real-time face detection algorithm developed by Viola and Jones (2004) is applied.
However, the face images obtained still contain some redundant areas that can impact
accurate recognition result and processing speed, so in the enhancement step, a cropping
technique is used as in following Figure 2.
Figure 2

Face region cropped by the cropping method
O(0, 0)
y = h/6
P(x, y)
x = (w1 – w2 ) / 2
Square S for cropping

w2
Human face image obtained from the
robust real-time face detector
w1

h


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The cropping method can be described as following:
•

First, the size of square S used for cropping the human face in images is determined.
The side w2 of square S will be equal to the widthwise of the human face. The size of
square S depends on each database even each image. However, based on tested
results of some databases by the image preprocess method as in Cao et al. (2013), the
widthwise of the human face accounts for from 75% to 85% of the widthwise of face
images obtained from the robust real-time face detector. It means that values of w2
are counted as experimental parameters.

•

Next step is to determine the coordinate P(x, y) from left-up corner of the image in
order to crop the square S. Let O(0, 0) is coordinate at left-up corner of human face
image obtained from the robust real-time face detector, h is the height of the face
image, w1 is the width of the face image and w2 is the width of the square S. So, the
coordinates are y = h/6 and x = (w1 – w2) / 2. Expression y = h/6 based on face
images having neutral facial expression. Normally, forehead region occupies
one-fourth of human face height. Thus, forehead region occupies a not small region
on human face region but it does not contain much essential information of face
expressions. For this reason, two-third (2/3) of upper forehead region is trimmed and
one-third (1/3) of lower forehead region from eyebrows is retained.

Finally, the human face image obtained from the robust real-time face detector is cropped
by square S at coordinate P(x, y). Figure 3 shows the cropping technique applied for a
face image.
Figure 3

Face region cropped by the cropping technique (the small square) (see online version
for colours)
Face region cropped by the robust
real-time face detector (large square)

This cropping method aims at reducing processing time in steps of feature extraction and
facial expression recognition, and most important being to improve the rate of facial
expression recognition. It is suitable for real time systems such as for intelligent
human-machine systems or intelligent game applications.

3

The CLBP for facial expression recognition

3.1 Local binary pattern
The local binary pattern (LBP) operator was first introduced as a complementary measure
for local image contrast as in Ojala et al. (1996). A LBP code is computed for a pixel in
an image by comparing it with its neighbours as in equation (1):


An effective facial expression recognition
P −1

LBPP , R =

∑s(g
p =0

p

⎧1, x ≥ 0
− g c ) 2c , s ( x ) = ⎨
⎩0, x < 0

227

(1)

where gc is grey value of the central pixel, gp is the grey value of its neighbours, P is the
total number of involved neighbours and R is the radius of the neighbourhood. Based on
the operator, each pixel of image is labelled by a LBP code.
For facial expression recognition, the uniform LBP code is usually used. A LBP code

is called uniform if it contains at most two bitwise transitions from 0 to 1 or vice versa
when the binary string is considered circular as in Ojala et al. (2002). For example,
00000000, 001110000 and 11100001 are uniform patterns. An uniform LBP operator is
denoted LBPPu,2R .
A histogram of a labelled image fk(x, y) can be defined as following:
Hi =

∑ I ( f ( x, y ) = i ) ,
k

i = 0,… , n − 1

x, y

(2)

where n is the number of different labels produced by the LBP operator and

⎧1 A is true
I ( A) = ⎨
⎩0 A is false

(3)

This histogram contains information about the distribution of the local micro-patterns,
e.g., spots, edges, corners or flat areas, etc., over the whole image.

3.2 Local difference sign-magnitude transform
According to Guo et al. (2010), based on a central pixel gc and its P circularly and evenly
spaced neighbours gp, p = 0, 1, …, P – 1, the difference between gc and gp can be

calculated as dp = gp – gc. The local difference vector [d0, …, dp–1] describes the image
local structure at gc and can be decomposed into two components:
⎪⎧ s p = sign ( d p )
d p = s p * m p with ⎨
⎪⎩m p = d p

(4)

⎧1, d p ≥ 0
is sign of dp and mp is the magnitude of dp. The equation (4) is
where s p = ⎨
⎩0, d p < 0

called the local difference sign-magnitude transform and it transforms the local difference
vector [d0, …, dp–1] into a sign vector [s0, …, sp–1] and a magnitude vector [m0, …, mp–1].
Figure 4 shows an example of the transformation.


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Figure 4

(a) A 3 × 3 sample block (b) Local difference (c) Sign component (d) Magnitude
component

25

48

76

–7

19

32

41

–13

36

87

9

4

(a)
0

44
9

55

–23

(b)

1

0
1

16

1

(c)

1

7

1

13

0

4

16

44
9

55

23

(d)

3.3 Completed LBP with CLBP_S and CLBP_M operators
The transformation shows that the original LBP uses only the sign vector to code the
local pattern because it is proved that dp can be more accurately approximated by using
the sign component sp than the magnitude component mp. However, it is also found that
the magnitude component may contribute additional discriminative information for
pattern recognition if it is properly used.
The sign component is the same as the original LBP operator defined in equation (1).
In CLBP, this component is denoted CLBP_S operator, whereas the magnitude
component is continuous values as a replacement for the binary ‘1’ and ‘0’ values. To
code this component in a consistent format with that of sign component to exploit their
additional information, the magnitude component is denoted CLBP_M operator and
defined as in equation (5):
P −1

CLBP _ M P , R =

∑t (m , c) 2
p

p =0

p

⎧1, x ≥ c
, t ( x, c ) = ⎨
⎩0, x < c

(5)

where the threshold c is to be determined adaptively and set as the mean value of mp from
the whole image. As the same uniform LBP operator, uniform CLBP_MP,R operator is
denoted CLBP _ M Pu ,2R .
Two CLBP_S and CLBP_M operators have same binary string format, so they can be
used together for pattern recognition. In proposed method, to form a CLBP descriptor,
histograms of CLBP_S and CLBP_M codes of the image are made by concatenation. It
means that the histograms of the CLBP_S and CLBP_M codes are calculated separately,
and then concatenate the two histograms together. This CLBP scheme can be represented
as ‘CLBP_S_M’.

3.4 Extracting CLBP feature for facial expression recognition
In facial expression recognition application, in order to represent the face efficiently,
features extracted should retain spatial information. For this reason, the face image can be


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229

divided into small regions before extracting feature. There have been proposed methods
for resizing and dividing the face images, for example, 110 × 150 pixels with 6 × 7
regions shown in Figure 5(a) as in Shan et al. (2005, 2009), Zhao and Zhang (2012) or
256 × 256 pixels with 3 × 5 regions shown in Figure 5(b), as in Ying et al. (2009), or
64 × 64 pixels with eight regions shown in Figure 5(c) as in Liao et al. (2006).

Figure 5

Proposed methods for resolution and region division

(a)

(b)

(c)

After the face images cropped, they are resized to resolution of 64 × 64 pixels as in Cao
et al. (2014), then the resized face images are divided into non-overlap regions of 8 × 8
pixels for extracting features. Next, each region is calculated CLBP histogram or CLBP
feature as in Figure 6. The CLBP features extracted from each region are concatenated
from left to right and up to down into a single feature vector of the face image.
Figure 6

Calculating CLBP histogram for face image

3.5 Choosing effective threshold for CLBP_M
Originally, CLBP was developed from LBP in order to obtain better result for texture
classification, especially in case of rotation invariant texture classification. They have
been effectively used for facial expression recognition in recent. The face image is
divided into regions before extracting feature vector in facial expression recognition.
Since the textures of regions in a face image are different, so we tested some different
thresholds c as following:
•

the mean value of mp from the whole image

•

the mean value of mp from the region

•

the mean value of mp from CLBP _ M Pu ,2R operator.

Experiment results on both JAFFE and CK databases show that choosing the threshold as
in the last case obtains the best accurate rate in facial expression recognition.


230

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N.T. Cao et al.

Experiments and results

We applied the proposed method on two databases. First database is JAFFE (JAFFE)
database. JAFFE database in Lyons et al. (1999) includes 213 grey images of ten JAFFE.
Original images from the database have a resolution of 256 × 256 pixels. In our
experiments, we selected all 213 images as experiment samples. Second database is the
CK database as in Kanade et al. (2000) and Lucey et al. (2010). The CK database consists
of 100 university students aged from 18 to 30 years, of which 65% were female, 15%
were African-American, and 3% were Asian or Latino. Subjects were instructed to
perform a series of 23 facial displays, six of which were based on description of basic
emotions (anger, disgust, fear, joy, sadness, and surprise). Image sequences from neutral
to target display were digitised into 640 × 490 pixel arrays with eight-bit precision for

greyscale values. In CK database, many subjects do not express all six primary emotions.
For our experiments, we chose subjects expressed at least three emotions (included
neutral state). So, 86 subjects (56 females and 30 males) from the database are selected.
Each primary emotion of a subject includes six images with expression degrees from less
to more and the neutral emotion is selected from some first images from the sequences.
Totally, 2040 images (234 anger images, 276 disgust images, 150 fear images, 390 joy
images, 474 neutral images, 156 sadness images, and 360 surprise images) are selected
for the experiments.
In classification step, support vector machine (SVM) classifier is applied since many
applications have confirmed SVM obtaining high results for classifying facial expression
as in Priya and Banu (2012) and Ahsan et al. (2013). We used SVM functions with
Radial Basis Functions kernel of OpenCV 2.1. In order to choose optimal parameters, we
carried out grid-search approach as in Hsu et al. (2010). Three-fold cross-validation
method is applied for experiments on platform C++ for both databases.
Confusion matrix of JAFFE database and confusion matrix of CK database are shown
in Tables 1 and 2, respectively. In these experiments, we used the percentage w2 / w1 of
cropped image on preprocess step is 80% and the threshold c for CLBP _ M Pu ,2R operator
is the mean value of mp from CLBP _ M Pu ,2R operator.
Table 1

Confusion matrix of JAFFE database at 80% of percentage w2 / w1
Anger (%)

Disgust
(%)

Fear (%)

Joy (%)

Neutral
(%)

Sadness
(%)

Surprise
(%)

Anger

96.67

0.00

0.00

0.00

0.00

3.33

0.00

Disgust

3.33

96.67

0.00

0.00

0.00

0.00

0.00

Fear

0.00

0.00

93.94

0.00

0.00

3.03

3.03

Joy

0.00

0.00

0.00

100.00

0.00

0.00

0.00

Neutral

0.00

0.00

0.00

0.00

100.00

0.00

0.00

Sadness

0.00

0.00

6.67

0.00

3.33

90.00

0.00

Surprise

0.00

0.00

0.00

3.33

0.00

0.00

96.67

Average:

96.28


An effective facial expression recognition
Table 2

Anger

231

Confusion matrix of CK database at 80% of percentage w2 / w1
Anger (%)

Disgust
(%)

Fear (%)

Joy (%)

Neutral
(%)

Sadness
(%)

Surprise

(%)

100

0.00

0.00

0.00

0.00

0.00

0.00

Disgust

0.00

100

0.00

0.00

0.00

0.00

0.00

Fear

0.00

0.00

100

0.00

0.00

0.00

0.00

Joy

0.00

0.00

0.00

100

0.00

0.00

0.00

Neutral

0.00

0.00

0.00

0.21

90.79

0.00

0.00

Sadness

0.00

0.00

0.00

0.00

0.00

100

0.00

Surprise

0.00

0.00

0.00

0.00

0.83

0.00

99.17

Average:

99.85

As we presented in Section 3.5, there are some ways to choose the threshold c for
CLBP_M operator, our experiments show that choosing this value is the mean value of
mp from CLBP _ M Pu ,2R operator gets the best results on both JAFFE and CK databases.
Table 3 presents the recognition rate of two databases using various thresholds. Figure 7

illustrates the results of using three kinds of threshold choosing in a chart.
Table 3

Recognition rate using various thresholds on CK and JAFFE databases
Recognition rate
of CK (%)

Threshold

Recognition rate
of JAFFE (%)

The mean value of mp from the whole image

99.72

95.32

The mean value of mp from the region

99.68

93.85

The mean value of mp from the CLBP _ M Pu ,2R operator

99.85

96.28

Figure 7

The chart of the results comparing various thresholds (see online version for colours)


232

N.T. Cao et al.

It is almost impossible to cover all of the published works. However, for comparison, we
would like to present several typical papers that represent state-of-the-art methods of
facial expression recognition whereby an overview of the existing methods is presented.
The comparison of a number of state-of-the-art methods with proposed approach on
JAFFE database and CK database is presented in Table 4 and Table 5, respectively.
Table 4

Comparison of the state-of-the-art methods with proposed method on JAFFE database

Classifying methods
Kind of feature

Feng et al.
(2007)

Shih et al.
(2008)

Lina and
Pan (2009)

Zhao and
Zhang (2012)

LPTa

SVM

SVM

1-NNc

SVM

2DPCA,
LBP

DKLLEd

CLBP

b

Proposed
method

LBP

2D-LDA

7

7

7

7

7

213

213

211

213

213

Cross validation test

10-fold

10-fold

10-fold

10-fold

3-fold

Recognition rate (%)

93.80

94.13

87.90

84.06

96.28

No. of facial
expressions
Number of images

a

Notes: LPT: linear programming technique.
b
2D-LDA: 2D-linear discriminant analysis.
c
1-NN: 1-nearest-neighbour.
d
DKLLE: discriminant kernel locally linear embedding.
Table 5

Comparison of the state-of-art methods with proposed method on CK database

Classifying methods

Ahsan et al.
(2013)

Shan et al.
(2009)

SVM

SVM

Zhao and
Khan et al.
Zhang (2012)
(2013)
1-NNc

Proposed
method

SVM

SVM

Gabor wavelet
and LTPa

BLBP

DKLLEd

PLBPe

CLBP

7

7

7

6

7

Number of images

1,632

1,280

1,409

309
sequence

2,040

Cross validation test

7-fold

10-fold

10-fold

10-fold

3-fold

Recognition rate (%)

96.90

91.40

95.85

96.70

99.85

Kind of feature
No. of facial
expressions

b

a

Notes: LTP: local transitional pattern.
b
BLBP: boosted-LBP.
c
1-NN: 1-nearest-neighbour.
d
DKLLE: discriminant kernel locally linear embedding.
e
PLBP: pyramid of LBP.

5

Conclusions

We presented a novel experimental method of facial expression recognition based on the
proposed image preprocessing technique and the improvement of a CLBP model. Our
experiments showed that a suitable threshold selected in computation CLBP can obtain
better recognition rate in facial expression recognition application. Based on the


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233

experiments, the accuracy recognition rate of JAFFE database is 96.28% and CK
database is 99.85%. Moreover, since the proposed method is very simple, fast and obtain
high accurate even with smaller resolution (e.g., 48 × 48 pixels), so it is suitable for real
time systems such as data-driven animation, intelligent game applications and intelligent
human-machine interface systems.

Acknowledgements
This work was supported by Basic Science Research Program through the National
Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future
Planning (2013R1A1A2012012). We would like to thank Professor Michael J. Lyons for
the use of JAFFE database, Professor Jeffery Cohn for authorising us to use
CK database in this work.

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