PSYCHOLOGICAL ANALYSIS OF AUTONOMOUS LEARNING BASED ON FACIAL EXPRESSION RECOGNITION

Revista Argentina de Clínica Psicológica 2020, Vol. XXIX, N°2, 1531-1538

DOI: 10.24205/03276716.2020.399 1531

P

SYCHOLOGICAL

A

NALYSIS OF

A

UTONOMOUS

L

EARNING

B

ASED ON

F

ACIAL

E

XPRESSION

R

ECOGNITION

Yongjun Liu

Abstract

With the proliferation of online learning platform, autonomous learning is increasingly convenient for students. However, the emotions of students may change in online learning environment, bringing uncertainties to the learning effect. This paper carries out a psychological analysis of autonomous learning in online environment based on facial expression recognition. First, a facial expression recognition framework was established, including modules like facial expression acquisition, image pre-processing, feature extraction, and feature classification and discrimination. Then, the independent component analysis (ICA) was adopted to interpret the Gabor feature vector, and an improved ICA facial expression recognition algorithm was designed based on Gabor wavelet transform, with the aim to recognize images accurately while eliminating high-order statistical redundancy. To verify its performance, the proposed algorithm was applied to match expression features and analyse the psychological changes of randomly selected images from the online teaching library. The results show that our algorithm can recognize the facial expressions accurately in online autonomous learning environment. The research findings help students cope with emotional changes in autonomous learning.

Key words: Facial Expression Recognition, Psychology, Independent Component Analysis (ICA), Gabor Wavelet Transform, Emotions.

Received: 18-05-19 | Accepted: 12-08-19

INTRODUCTION

With the continuous advancement of science and technology and educational concepts, the application of the on-line teaching platform has been unanimously recognized by students and teachers. Such platform can ensure the efficient learning over time and place, while providing novel teaching modes and vivid teaching content (Cascella & Jez, 2018). However, under this kind of learning environment, there is the lack of the effective communication between teachers and students, between students, and emotional transmission as in the traditional learning mode,

which is not conducive to the students’ physical

and mental development. Facial expression recognition technology (Benkaddour & Bounoua,

College of Foreign Languages, Hainan University, Haikou 570228, China

E-Mail: [email protected]

2017; Fourati & Richard, 2018) has a wide range of applications and advantages in image processing, pattern identification, physiology and psychology; it has a guiding significance for

the study of students' emotional and

psychological changes in the web-based autonomous learning environment.

For facial expression recognition, there are a variety of mature algorithms in the extraction of expression features, face detection and expression classification etc., especially the use of ICA method provides an effective technical support for the extraction of expression features in the mouth, eyebrows, and eyes, and also the recognition of expressions, which plays an important role in the in-depth study of

emotional changes and psychoanalysis

(Seghouane, & Iqbal, 2017). However, the ICA only calculates the second-order statistic of the images, but cannot accurately achieve the

YONGJUN LIU

1532

psychological changes due to the problem of high-order statistical redundancy and reduced image recognition caused by illumination and sound etc. Therefore, there is still much room for improvement in the ICA method for face recognition.

In view of the high-order statistical redundancy and low image recognition problems in the ICA method mentioned above, this paper

proposes an improved ICA expression

recognition algorithm based on Gabor wavelet transform, which can both increase image recognition rate and eliminate high-order statistical redundancy. Then, the expression recognition framework was built through the steps of expression acquisition, image pre-processing, feature extraction, and classification discrimination, and the ICA function was used to interpret the Gabor feature vector, in order to effectively detect and extract facial expression features. Finally, using the facial expressions of students in the network teaching database as experimental materials, the improved ICA expression recognition algorithm was applied to extract the features of facial expressions and

analyse the changes of emotions and

psychology. The results show that this method can accurately analyse the psychological changes of students. It is of great significance for deep

understanding of students' emotion

communication and changes.

FACIAL EXPRESSION RECOGNITION

FRAMEWORK

The facial expression recognition mainly includes expression acquisition, image pre-processing, feature extraction, and classification discrimination, which can constitute the facial expression recognition framework, as shown in Figure 1.

The expressions acquisition can be made by geometric features, correlation matching, subspace and statistical methods to capture the position and shape of important organs such as the eyes, eyebrows, nose and mouth of the face; the image pre-processing usually use the histogram equalization method to calculate the grey distribution, and the brightness and contrast of the image are reflected according to the frequency of the grey value; the feature extraction transforms the pre-processed image into advanced features such as texture and shape; it is the most core part of the expression recognition algorithm which generally divides the facial expression into six types of happiness, fear, sadness, anger, surprise and hate (Loth, Garrido, Ahmad et al., 2018). These six types of facial expressions are respectively described in Table 1.

Figure 1

.

Facial Expression Recognition Framework

Expression acquisition

Image preprocessing

Feature extraction

Classification and discrimination

Table 1. Specific description of six facial expressions

Expression Facial description

Happy

The eyebrows are slightly bent down; the lower part of the eyes may be bulging and wrinkled; the mouth may grow and the teeth are exposed; the lip angle is raised and raised backwards; the cheeks are raised;

there may be a wrinkle extending from the nose to the corner of the mouth.

Fear Eyebrows gather in the middle; mouth open, lips slightly tense; upper eyes raised, lower eyelids tightened; forehead wrinkles tightened.

Sadness The inner corners of the eyes droop; the corners of the mouth drop down and may tremble; the eyebrows are too high and wrinkled, and the skin under the eyebrows is tightened.

Anger Eyes wide open; mouth closed or pulled down vertically; eyebrows folded together and pressed down; wrinkles between eyebrows; nostrils may widen; eyelids under the eyes tight, too high and wrinkled.

Surprise Eyes open, upper eyelids raised, lower eyelids drooping; white eyeballs exposed more than usual; mouth open, teeth and lips separated; eyebrows raised, the skin below was stretched.

Hate Cheeks and noses raised; eyebrows drooping, wrinkles under eyelids; cheeks raised upward; lips closed, corners of the mouth pulled down; upper lip raised, lower lip raised.

PSYCHOLOGICAL ANALYSIS OF AUTONOMOUS LEARNING BASED ON FACIAL EXPRESSION RECOGNITION 1533

After the feature extraction of the facial expression, it can be classified and discriminated

from the expression database generally

according to the hidden Markov model and the neural network method (Neelapu, Devi, & Rao, 2018; Pedram, Brian, Tammy et al., 2017). Through the above four steps, it is possible to accurately extract and classify facial expressions.

FACIAL EXPRESSION RECOGNITION BASED ON ICA

ICA principle

For any blind source signal 𝑆 =

{𝑠1, 𝑠2, . . . 𝑠𝑛}𝑇, there are

m

signals, and each group of signals can be represented by a row vector with dimension 1 (Hsiao Chang, Tsai et al., 2017), so the matrix

𝐴𝑚𝑛 satisfies the linear relationship between

the blind source signal 𝑆 and the observed signal 𝑋:

X

=

AS

There is a mixing matrix 𝑊 that allows the output matrix 𝑌 to be closest to the estimate

𝑆:

Y

=

WX

=

WAS



S

𝑊𝐴 = 𝐼 and 𝐼 is the unit matrix.

The transmission between blind source

signals is unordered, independent and

uncorrelated. Compared with traditional

principal component analysis, ICA has a higher recognition rate of the facial expressions. The process of its independent component is shown in Figure 2.

Figure 2

.

Independent Component Process

Diagram

A X W Y

S

ICA preprocessing

In order to improve the accuracy of the operation and reduce the complexity of the algorithm, the original data should be pre-processed to solve the independent components of ICA algorithm. It usually includes the centralization and whitening.

(1) Centralization

The centralization is to subtract the average of each group of raw data into a zero-mean variable, so the centralization is also called the de-mean. For any raw data 𝑥′, it’s shown as:

{ }

x

= −

x



E x



When the original data is zero mean, each corresponding blind source signal also becomes a zero-mean vector. It’s calculated as:

( { }) { } { }

y Wx W x= = −E x =Wx−E Wx = −y E y ₍₄₎

In the above derivation process, the matrix

𝐴𝑚𝑛 remains unchanged, i.e., the centralization

operation does not have a direct impact on the estimated value of 𝐴𝑚𝑛. After the centralization

of the original data, the blind source signal can be obtained by adding 𝑊𝐸{𝑥′}.

(2) Whitening

Whitening is to remove the correlation of the observed signals, so that they have certain unit variance. It is common practice to perform singular value decomposition operations on the covariance matrix of any vector 𝑥, and then obtain the whitening vector 𝑥̃:

1 2 T

x

%

=

ED E x

Similarly, the matrix 𝐴𝑚𝑛 in the ICA

algorithm is converted into 𝐴̃𝑚𝑛:

1 1

2 T 2 T

x

%

=

ED E x

=

ED E As

=

As

%

The matrix 𝐴̃𝑚𝑛 obtained by equation (6) is

an orthogonal matrix, in which the range in the ICA algorithm is reduced, so the whitening operation effectively reduces the amount of computation (Sasaki Gutmann, Shouno et al., 2017)

Non-Gaussian indicators

Non-Gaussianity usually uses the fourth-order statistic kurtosis as an indicator. Assuming that the kurtosis 𝑘𝑢𝑟𝑡(𝑦) of the random variable

y

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1534

4 2 2

( ) { } 3( { })

kurt y =E y − E y ₍₇₎

At 𝑘𝑢𝑟𝑡(𝑦) > 0, the random variable 𝑦 is strictly subject to the sub-Gaussian distribution; at 𝑘𝑢𝑟𝑡(𝑦) < 0,𝑦 strictly obeys the super-Gaussian distribution. Figure 3 shows their distribution shapes.

From Figure 3, it can be seen that the random variable 𝑦 of the sub-Gaussian is in a “flat”

shape, close to the common Gaussian shape

distribution; the super-Gaussian random

variable 𝑦 has a “steepled thick edge” shape,

similar to the Laplacian shape distribution.

Figure 3

.

Shape distribution maps of

super-gaussian, Gaussian and sub-Gaussian

Gaussian distribution Super Gaussian

distribution

Subgaussian distribution

ICA-based facial expression recognition algorithm

According to the ICA-based facial expression recognition algorithm, the original data should first be initialized as 𝑤1, 𝑤2, . . . 𝑤𝑛 for image

processing of facial expressions; the grey matrix of the image is centralized and whitened, and the independent component is determined m according to the non-Gaussian indicator; afterwards, an independent analysis algorithm can be used to perform orthogonalization and standardization processing until the vector

𝑤𝑝converges (Kobayashi & Iwai, 2018). It can be

summarized into the following eight steps: (1) Perform centralization and whitening operations on the processed face image matrix to obtain 𝑥;

(2) Determine the number of independent

components m according to non-Gaussian

performance indicators, and let 𝑝 ← 1;

(3) Randomly select the initialization vector

𝑤𝑝 in the image matrix;

(4) Use 𝑤𝑝 as the pass parameter to

complete the independent analysis algorithm:

{

(

)}

{ (

)}

;

p p p p

w



E xg w x

−

E g w x w



(5) Perform orthogonalization:

1

1

(

)

;

p _T

p p _j p j j

w



w

−



w w w

(6) Standardize the 𝑤𝑝, and then 𝑤𝑝←

𝑤𝑝/‖𝑤𝑝‖;

(7) If 𝑤𝑝 doesn’t converge, then return to

(4);

(8) Let𝑝 ← 𝑝 + 1; if 𝑝 ≤ 𝑚, return to (3).

IMPROVED ICA FACIAL EXPRESSION

RECOGNITION ALGORITHM BASED ON GABOR WAVELET TRANSFORM

Gabor wavelet transform

The two-dimensional Gabor wavelet

transform plays an important role in the image analysis. The collected signal is processed using a filter function to make it gradually approximate to a standardized signal. The Gabor filter function is expressed as:

2 2 2

2

2 2

( ) ( ) ( )

2 2

j j j

j j

k k x

x exp exp ik x exp 



 

 _{ }

 

= _− _ − − _

   

 

r r _r

r

r r

(8)

jx v u

j

jy u jy

k

k cos

k

k sin

k







 



=



_

 

=



 







r

(9)

Where, 𝑥⃗ is the coordinate of the image matrix;

𝑘⃗⃗𝑗 is the frequency vector, which can reposition

the orientation and scale of the Gabor; 𝜑𝑢 is

the orientation of the filter; ‖𝑘⃗⃗𝑗‖

2

𝜎2 is the

difference of the energy attenuation to be

compensated; 𝑒𝑥𝑝 (−‖𝑘⃗⃗𝑗‖ 2

‖𝑥⃗𝑗‖2

2𝜎2 ) is the defined

range of the Gaussian function in the plane coordinate, and 𝑒𝑥𝑝(𝑖𝑘⃗⃗𝑗𝑥⃗) is the plane wave of

the 𝑘⃗⃗𝑗 and 𝑥⃗, in which the imaginary part is

𝑐𝑜𝑠(𝑘⃗⃗𝑗𝑥⃗), the real part is 𝑠𝑖𝑛(𝑘⃗⃗𝑗𝑥⃗).

The real part of the plane wave complex value is oddly symmetric in the Gaussian normal distribution, and within the limited range of the

PSYCHOLOGICAL ANALYSIS OF AUTONOMOUS LEARNING BASED ON FACIAL EXPRESSION RECOGNITION 1535

Gaussian function, its integral value is 0; the imaginary part of the plane wave complex value is evenly symmetric in the Gaussian normal distribution, and within the limited range of the Gaussian function, its integral value is not zero. Therefore, in order to eliminate the influence of the image DC component in the Gabor wavelet

transform, 𝑒𝑥𝑝(−𝜎2

2) is removed from the real

part of the plane complex value, so that the grey value of the signal image generated by the Gabor filter is not affected, and the error caused by noise such as illumination and sound is reduced (Samiee, Kovács, & Gabbouj, 2017). According to the local parity of the Gaussian function, the extraction of the image feature is roughly in the vicinity of 𝑥⃗. Taking it as a Gabor transform, a self-similar Gaussian function group can be formed through the flipping, translation, scale deformation, etc. of 𝜓𝑗, that is, satisfying the

characteristics of Gaussian filter bank. Finally, it is assumed that all grey values in the image matrix are equal, according to the properties of the Gabor wavelet transform function it’s derived as:

2

( ) (

)

0

I x





x

−

x d x





=



r

r

r

r

Gabor features of facial expressions

The feature of facial expressions is no longer extracted by ICA, but Gabor wavelet transform is used to obtain Gabor features (Pathan, Siddalingaswamy, & Prabhu et al., 2017). It is assumed that for any facial expression image

𝐼(𝑧), the convolution operation of the wavelet function 𝜑𝑢,𝑣 is implemented for 𝐼(𝑧) by the

Gabor wavelet transform method. It’s calculated

as:

,

( )

( )

( )

u v u v

G

z

=

I z





z

(11)

In the formula above, 𝐺𝑢,𝑣(𝑧) is formed

under the deformation of orientation 𝑢 and scale

v

form of complex numbers; the phase and amplitude information of the image grey matrix can be obtained from the real part and the imaginary part respectively:

1

, ( ) ( ( , ( )) / ( , ( )))

u v u v u v

P z =tan− Im G z Re G z

(12)

2 2

, ( ) ( , ( )) ( , ( ))

u v u v u v

M z = Re G z +Im G z

(13)

In order to obtain a uniform sampling signal in Gabor's feature extraction, the paper uses a Gabor wavelet function 𝑏 = 𝑡𝑎𝑛−1_{𝑎 consisting}

of 8 orientations 𝑢 = {0,1,2,3,4,5,6,7} and 5 scales 𝑣 = {0,1,2,3,4}, and the wavelength ratio between each adjacent scale is 𝑓 = √2 . Therefore, in any facial expression image 𝐼(𝑧), 40 subgraphs were obtained by interpreting the orientation 𝑢 and scale 𝑣 (Oh, Toh, Teoh et al., 2018).

The amplitude parameter is an important basis in the pattern recognition of images. Compared with the independent phase-based expression recognition algorithm, the amplitude has higher recognition in images, but the role of the phase in facial expression cannot be ignored. Therefore, when the two images of the grey matrix have the same amplitude, they must rely

on different phase parameters for

discrimination; when they have the same phase, they must rely on different amplitude parameters. Gabor feature description of facial expressions can only be obtained by combining amplitude and phase.

Improved ICA expression recognition algorithm based on Gabor wavelet transform

Based on Gabor wavelet transform, the ICA algorithm was improved for facial expression feature extraction, and convolution calculation of Gabor function was made by combining the amplitude and phase parameters, to perform centralization and whitening processing on the amplitude matrix and phase matrix, and obtain the feature matrix and separation matrix (Alphonse & Dharma, 2017). Finally, the Euclidean distance was used to judge and classify the facial expressions, that is, the image with the minimum phase and Euclidean distance is the matching one. The specific algorithm includes the following five steps:

(1) The convolution computation is

implemented for a total of 40 Gabor wavelet functions from 8 orientations and 5 scales and the database sample image 𝐼𝑚,𝑛, to obtain

phase and amplitude parameters, and then the magnitude vector 𝑎 and phase vector 𝑏. The eigenvectors of 40 Gabor wavelet functions are then combined into an amplitude feature matrix

𝑀 and a phase feature matrix 𝑃. For the image sample set in the database, the feature matrix of

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amplitude and phase are calculated separately to form a new set {𝑥𝑘} and {𝑦𝑘}, where 𝑘 =

1,2, . . . , 𝑙;

(2) Calculate the amplitude and phase characteristic matrix 𝑀1 and 𝑃1 of the image

set separately;

(3) Take the data set {𝑥_𝑘}composed of the amplitude feature matrix as the original data, use the ICA algorithm to obtain a separation matrix 𝑊;

(4) Project the data set {𝑥𝑘} and the

separation matrix 𝑊 composed of the

database sample images to obtain the projection vector 𝑌1, 𝑌2, . . . 𝑌𝑙 of each independent data and

form a new vector set

Y

(5) Calculate the Euclidean distance between

𝑌 and 𝑌1, 𝑌2, . . . 𝑌𝑙 using the distance formula,

select the minimum Euclidean distance as the reference value, and then select the projection vector with the smallest phase from the minimum Euclidean distance group. Thus, the image with the minimum Euclidean distance and the smallest phase is the one with the highest matching degree in the expression database (Upla, Joshi, & Khatri, 2017)

EXPERIMENTAL RESULTS AND ANALYSIS In this experiment, a total of 140 images for 7 types of facial expressions were randomly selected from the online teaching library as

experimental data. The data was first

decomposed by Gabor wavelet. Then, the three

orientation parameters and three scale

parameters were selected to identify six expressions such as happiness, fear, sadness, anger, surprise and hate, and determine the optimal orientation and scale combination parameters. At last, the ICA algorithm, Gabor wavelet transform algorithm and the improved ICA expression recognition algorithm based on Gabor wavelet transform were used to identify the experimental data.

First, select 𝑢 = {1,4,6} from 8 orientations as the orientation parameter, and 𝑣 = {0,1,3}

from 5 scales as the scale parameter, and then test the 140 images. Table 2 lists the recognition rate of the images.

It can be seen from Table 2, when the orientation and scale are 4 and 1, respectively, the recognition rate of six expressions such as happiness, fear, sadness, anger, surprise and hate is higher than other parameters, that is, when there are great changes in the mouth, eyes, and eyebrows, the recognition degree of the image is better; when the eye corner, the eyebrow, the corner of the mouth, the nose, the cheek, etc. are slightly changed, the recognition degree is lower. Therefore, with the orientation parameter of 4 and the scale parameter of 1, the recognition of the facial expression can reach

98.27%, so that the students’ expression under

the network environment can be accurately judged. Furthermore, it provides an important

basis for the emotional analysis and

psychological changes (Anne & Huxhold, 2018).

Table 2.

Statistical Table of Recognition Rate Results of Images in Different orientations and

Scales

Orientation Scale Happy Fear Sadness Anger Surprise Hate Average

1 0 94.5% 90.1% 89.5% 100% 86.9% 96.8% 92.97%

1 1 97.2% 95.4% 94.4% 89.5% 93.8% 90.5% 93.47%

1 3 89.5% 97.2% 93.7% 96.7% 91.5% 100% 94.77%

4 0 98.5% 91.1% 96.3% 95.2% 98.2% 86.4% 94.28%

4 1 100% 98.9% 97.5% 99.9% 97.5% 95.8% 98.27%

4 3 100% 94.6% 90.0% 95.3% 85.6% 96.8% 93.72%

6 0 92.5% 95.3% 95.5% 86.9% 100% 88.4% 93.10%

6 1 99.5% 90.8% 93.8% 92.7% 87.5% 95.8% 93.35%

6 3 97.3% 93.4% 92.9% 100% 88.6% 94.4% 94.43%

Table 3. Statistical table of expression recognition rate under different algorithms

Algorithm Happy Fear Sadness Anger Surprise Hate Average

ICA 95.3% 98.6% 94.8% 93.2% 94.6% 95.9% 95.40%

Gabor 96.1% 92.4% 97.9% 99.9% 98.1% 97.6% 97.00% Gabor + ICA 100% 96.6% 98.1% 99.8% 97.3% 100% 98.63%

PSYCHOLOGICAL ANALYSIS OF AUTONOMOUS LEARNING BASED ON FACIAL EXPRESSION RECOGNITION 1537

In addition, the ICA algorithm, Gabor wavelet transform algorithm, and Gabor wavelet transform based improved ICA expression recognition algorithm were used to identify the experimental data. Table 3 lists the recognition rate of the obtained images.

From the statistical results of the facial expression recognition rate in Table 5-2, the improved ICA expression recognition algorithm based on Gabor wavelet transform has obvious advantages, and its recognition rate can reach 98.63%. Then, this improved algorithm can be applied to the student's self-learning in the network environment. Through the images transmitted by the video, it can analyse the

students’ facial expression of the student, and

calculate the emotional and psychological changes. This is of great significance to improve

the student's emotional control and

psychological construction, and promote the growth of students' physical and mental health (Shai, Johanna, & Giora, 2018).

CONCLUSIONS

Based on the research of facial expression recognition technology, the paper builds the expression recognition framework through the steps of expression acquisition, image pre-processing, feature extraction, and classification and discrimination. Then, it proposes an improved ICA expression recognition algorithm based on Gabor wavelet transform, using the ICA function to interpret the Gabor feature vector, which is designed to effectively detect and extract facial expression features. The experimental results show that when the selected orientation and scale parameters of Gabor wavelet transform are 4 and 1, respectively, it can achieve a higher recognition rate for facial expression, which is of great significance for studying students' emotional changes and communication under the network learning environment. It also contributes to the development of students' physical and mental health, and plays an important role in improving students' psychological cognition and learning

effects. In addition, it’s expected that the research findings can promote the psychological research on students' self-learning in the online teaching environment.

Acknowledgement

The Study was supported by Hainan Federation of Social Science Circles, China (Grand No: HNSK(JD)17-44).

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