[IEEE 2013 IEEE International Conference on Control System, Computing and Engineering (ICCSCE) -...

Infant Pain Recognition System with GLCM features and GANN under Unstructed Lighting Condition

Muhammad Naufal Mansor School of Mehatronic Engineering

Universiti Malaysia Perlis Perlis, Malaysia

[email protected]

Mohd Nazri Rejab Department of Mechanical

Politeknik Tuanku Syed Sirajuddin Perlis, Malaysia

[email protected]

Abstract—This paper discussed the crucial demand regarding the scheme to translate the silence voice from the newborn. The infant can’t afford to express their feeling of pain by voice. Hence, we proudly present an infant pain recognition system to overcome this matter. We employed the Single Scale Retinex (SSR) to remove the illumination level. Secondly, Gray-Level Co-occurrence Matrix (GLCM) was adopted as the feature extraction. We determine the condition of the infants (pain/no pain) with Hybrid Genetic Algorithm Neural Network (GANN) and Linear Discriminant Analysis (LDA). Several examples were conducted to evaluate the performance of the proposed method under different illumination levels.

Index Terms—Infant Pain, SSR, GLCM, GANN.

I. INTRODUCTION

Some rigorous techniques have been proposed in the literature to cope with the problem of pain recognition [1-5]. However, based on the study, most researches only stick on new texture descriptors and a hybrid artificial intelligent [6-7] to solve the matters. They agreed that new features and hybrid classification methods offer complementary information about the patterns that enforce diversity fare better than other [8]. Constraint with theirs point of view, we believed that the illumination problem should be tackle first and common features and classifiers were sufficient to do the job.

Thus, within this paper, a new pre-processing method is presented for infant pain recognition under different level of illumination. In our method, the single scale retinex (SSR) algorithm is adopted for extracting illumination invariant facial features [9]. Compared with other methods, our approach can obtain the adaptive smooth images while preserving the illumination discontinuities. As a result, it can effectively reduce the halo artifacts in the normalized image. Finally, GLCM [11] was employed as feature extraction. The proposed method is tested with Hybrid Genetic Algorithm Neural Network (GANN) and Linear Discriminant Analysis (LDA) methods using the Infant Cope databases. The results demonstrate the effectiveness of our method, which can achieve satisfactory recognition rates with different illumination levels.

The organization of this paper is as follows. Sections II briefly describe our proposed algorithm empirically. In

Section III and IV, we will present GLCM feature extraction method and GANN and LDA classifier briefly. Sections V and VI show the comprehensive experiment results and our conclusion respectively.

II. PROPOSED METHOD

As illustrated in Fig. 1, the experimental procedures can be divided into the following stages: preprocessing, feature extraction, and classification. The original images are adopted from Classification of Pain Expressions (COPE) database. The Images are reduced to 100pixels x 120pixels, in order to reduce the time processing. In the preprocessing stage, we present our new combination of pre-processing method for infant pain recognition. In the feature extraction stage, facial features such as GLCM are employed as the feature vectors further. Finally, GANN and LDA are used to classify the feature vectors into the following category pairs: pain/nonpain. All experiments were processed in the MATLAB environment under Windows XP operating system using an Intel® Core ™ 2Duo CPU, 2.80 GHz processor.

Fig. 1. Infant pain Experimental Design

III. PRE-PROCESSING

The our main step involved 3 subsection such as (a) Image Subjects, (b) grayscale images (c) Illumination adding and finally (d) Single Scale Retinex (SSR).

GANN Classifier

Haralick

Image Subjects

Pre-Processing ~Grayscale Image ~Add Illumination

~Single Scale Retinex (SSR)

LDA Classifier

2013 IEEE International Conference on Control System, Computing and Engineering, 29 Nov. - 1 Dec. 2013, Penang, Malaysia

978-1-4799-1508-8/13/$31.00 ©2013 IEEE 243

A. Image Subjects The Infant COPE database contains a total of 204 color photographs of 26 Caucasian neonates (13 boys and 13 girls) ranging in age from 18 h to 3 days. Six males had been circumcised the day before the photographs were taken, and the last feeding time before the photography session ranged from 45 min to 5 h [1-3].

Fig. 2. Infant COPE Database

B. Grayscale Image We convert the true color image of the subjects to the grayscale intensity image. The grayscale image eliminating the hue and saturation information while retaining the luminance [12].

Fig. 3. Grayscale Infant COPE Database Image

C. Illumination Adding To simulate the problems associated with testing in different environments, illumination (varying across the image) was added to the test images of the COPE database. This was done by changing the intensity of the image’s pixels proportionally to their column displacement from the image centre [13]. An example of the effect of this transform (for differentδ ) on an image is shown in Figure 2. The illumination algorithm is constructed by [14]. New Image ),( xy = Old Image δ++ mxxy ),( (1)

Where 2/)1( −

−=XN

m δ

,1,....,1,0,1,....,1,0

−=−=

Y

X

NyNx

Fig. 4. Illumination for Infant COPE Database Image

D. Single Scale Retinex The single scale retinex (SSR) algorithm was proposed by Jobson et al. in [10]. The (SSR) Reflectance image is takes from the form.

)],(*),(log[),(log),( yxyxFyxyxRSSR Ι−Ι= (2) Where * = Convolution operation

),( xyF = Surround Gaussian Function Like the majority of photometric normalization techniques it is based on the so-called retinex theory which is explained in [9] in more detail.

Fig. 5. Single Scale Retinex for Infant COPE Database Image

IV. GLCM FEATURE EXTRACTION GLCM features are used for analyzing the texture of the face. The GLCM functions characterize the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image. The entire 14 different elements of GLCM features are derived as follows [11] Angular Second Moment


244

{ }2

1 11 ),(∑∑

= =

=Ng

i

Ng

j

jipf (3)

Contrast

njiwhen

jipnfNg

i

Ng

j

Ng

n

=−

⎟⎟⎠

⎞⎜⎜⎝

⎛= ∑∑∑

= =

−

= 1 1

1

0

22 ),(

(4)

Correlation

yx

Ng

i

Ng

jyxjipij

fσσ

μμ∑∑= =

−= 1 1

3

),()( (5)

Sum of Square Variance

),()(1 1

24 jipif

Ng

i

Ng

j∑∑

= =

−= μ (6)

Inverse Different Moment

),()(1 1

25 jipif

Ng

i

Ng

j∑∑

= =

−= μ (7)

Sum Average

)(2

26 iipf

Ng

iyx∑

=+= (8)

Sum Variance

)()(2

2

287 ipfif

Ng

iyx∑

=+−= (9)

Sum Entropy

{ })(log)(2

28 ipipf yx

Ng

iyx +

=+∑−= (10)

Entropy

)),(log(),(1 1

9 jipjipfNg

i

Ng

j∑∑

= =

−= (11)

Difference Variance

[ ] [ ]2210 yxyx pEpEf −− −= (12)

Difference Entropy

{ })(log)(1

011 ipipf yx

Ng

iyx +

−

=+∑−= (13)

Information Measures of Correlation I and II

{ }HYHXHXYHXYf,max

112

−= (14)

[ ]21

13 )2(2exp1( HXYHXYf −−−= (15)

Maximal Correlation Coefficient

=14f (Second Largest Eigen Value of Q) 21

(16)

Where

∑=

=Ng

jx jipip

1

),()( (17)

∑=

=Ng

jy jipip

1

),()( (18)

∑∑= =

−=Ng

i

Ng

j

jipjipHXY1 1

))),()(log(,( (19)

{ }))()()(log,(11 1∑∑

= =

−=Ng

i

Ng

jy jpipjipHXY

x (20)

{ }))()()(log()(21 1∑∑

= =

−=Ng

i

Ng

jyyx jpipjpipHXY

x (21)

∑=

=Ng

k yx kpipkjpkipjiQ

1 )()(),(),(),( (22)

=yx μμ , Mean

=yx σσ , Standard Deviation

=Ng Number of Gray Levels

=)(ipx thi entry obtained by summing the rows of ),( jip

V. CLASSIFIER

A. GANN Classifier This algorithm applies a tournament selection (ordinal based) and replacement consists on a steady state update also implemented with a tournament technique. The tournament size is 3. A hundred of generations for the genetic algorithm are carried out in every experiment. The best parameters for entire experiments were population size of 20, 0.8 crossover probabilities and the basic mutation probability of 0.001. Each neural network has different hidden layers and is trained over 200 epochs with back propagation. This value is higher than the one usually used to train neural networks, giving enough time to the training to converge, and so taking advantage of the whole potential of each network [15].


245

B. LDA Classifier Linear Discriminant Analysis (LDA)is based on Fisher criteria; it aims to project the samples onto a feature space, where the ratio of between-class scatter to the within-class scatter of the projected samples is maximized, so the samples in same classes will be gathered together while the samples in different classes will be loosened, and classification will be improved [16]. It is a method of dimension reduction which considers the information of sample’s categories. Suppose { }nixX i ,.......,2,1, = are sets of training data

Ni Rx ∈ and n is the total of samples, A projected sample is:

)( NmRx m

Li <∈ (23) N is samples features dimensions. So the aim of LDA is to find linear transformation matrix NxMRW ∈ to maximum the ratio of between-class scatter to the within-class scatter of the projected samples. let c be the total of classes. Between-class scatter matrix is defined as:

Ti

c

iib PiS ))((

1μμμμ −−=∑

=

(24)

Within-class scatter matrix is defined as:

Ti

c

i cixiw xx

nS ))((1

1μμ −−= ∑∑

= ∈

(25)

where nnP i

i = is priori probability of each class, in is the

number of training samples of class

),,.....,2,1(, cici = ∑∈

=cix

i xn1μ is sample mean vector of

∑= xn

ci1, μ is the average of all samples. The need of

LDA is to find the optimal projection optW to make:

WSWWSWWJ

WT

bT

opt maxarg)( = (26)

Accordingly, the projected sample can be expressed as: XWx T

optL = (27)

VI. EXPERIMENTAL RESULTS The proposed algorithm was evaluated on a COPE database. The entire subjects were tested for ten trials with GANN and LDA. Average accuracy and time consumption of GANN and LDA for all subjects were illustrated in Table I and Table II respectively.

TABLE I. GANN PARAMETER SETTING AND RESULTS FINDING

No of Trial GANN Parameter Setting

Illumination Levels Epoch

No. Hidden

layer

Time Process (s) Accuracy %

1 20 200 2 30.00 90.78 2 30.54 95.76 3 30.59 92.76 4 32.93 89.42 5 30.64 88.50 6 30.30 89.26 7 32.00 89.54 8 30.83 97.01 9 31.51 87.73

10 31.45 89.52 Average results for 20 Illumination Levels 31.00 91.02

1 40 200 4 30.81 88.83

2 30.68 82.08

3 30.61 80.89

4 30.63 85.67

5 30.68 82.42

6 30.91 83.27

7 30.56 88.97

8 30.56 80.79

9 30.59 83.71

10 30.58 86.53 Average results for 40 Illumination Levels 30.6 84.31

1 60 200 6 30.84 85.82

2 30.60 82.08

3 30.57 86.34

4 33.37 87.30

5 31.21 89.35

6 30.76 87.97

7 30.72 73.94

8 30.67 71.23

9 30.59 71.04

10 30.59 74.05

Average results for 60 Illumination Levels 31.02 80.91 1 80 200 8 30.78 87.87

2 30.58 88.34

3 30.64 78.64

4 30.66 70.02

5 30.59 79.68

6 30.67 87.42

7 30.71 81.31

8 30.67 58.83

9 30.60 33.02

10 30.61 64.38

Average results for 80 Illumination Levels 30.65 70.95 1 100 200 10 0.78 73.61

2 0.59 71.31

3 0.57 76.86

4 0.57 79.16

5 0.58 72.23

6 0.57 71.56

7 0.60 76.53

8 0.57 63.61

9 0.60 62.42


246

10 0.57 55.57

Average results for 100 Illumination Levels 30.60 70.28 0.57 59.29

TABLE II. LDA RESULTS FINDING

No of Trial LDA Results Finding Illumination Levels Time Process (s) Accuracy % 1 20 0.76 67.97 2 0.58 57.20

3 0.58 61.75

4 0.55 71.56

5 0.57 69.60

6 0.60 66.34

7 0.58 69.93

8 0.56 64.71

9 0.58 66.01

10 0.58 69.93 Average results for 20

Illumination Levels 0.60 66.50

1 40 0.81 58.83

2 0.68 62.08

3 0.61 70.89

4 0.63 65.67

5 0.68 62.42

6 0.91 53.27

7 0.56 66.97

8 0.56 60.79

9 0.59 63.71



1 60 0.84 75.82

2 0.60 62.08

3 0.57 66.34

4 3.37 67.30

5 1.21 49.35

6 0.76 67.97

7 0.72 53.94

8 0.67 71.23

9 0.59 81.04



1 80 0.78 57.87

2 0.58 66.34

3 0.64 68.64

4 0.66 50.02

5 0.59 49.68

6 0.67 45.42

7 0.71 51.31

8 0.67 58.83

9 0.60 33.02



1 100 0.78 53.61

2 0.59 51.31

3 0.57 56.86

4 0.57 59.16

5 0.58 72.23

6 0.57 71.56

7 0.60 56.53

8 0.57 53.61

9 0.60 62.42



Average Accuracy Vs Different Illumination Levels

0102030405060708090

100

20 40 60 80 100

Illumination LevelsAv

erag

e Ac

cura

cy (%

)

Average Accuracy for GANN (%)Average Accuracy for LDA (%)

Fig. 6. Average Accuracy Vs Different Illuminations Levels

Figure 6 shows the comparison results of Average Accuracy vs. Different Illumination Levels between GANN and LDA Classifier. Based on the experiment GANN gives the best accuracy for all the trials because it is apparent that the highest levels of accuracy are in the range 91.00% to 70.00% in average accuracy when the illumination levels increase from 20 to 100δ . For LDA classifier, the highest percentage accuracy is 66.50 % at 20δ . However 54.55 % at 80δ is the lowest percentage average accuracy among others. Finally in all of trials, it determined that GANN give the consistent values compared to LDA classifiers.

Average Time Taken Vs Different Illumination Levels

0

5

10

15

20

25

30

35

20 40 60 80 100

Illumination Levels

Tim

e ta

ken

(s)

Average Time(s) Takenfor GANNAverage Time(s) Taken for LDA

Fig. 7. Average Time Taken Vs Different Illuminations Levels

Figure 7 shows Average Time Consumption for Different Illumination Levels for GANN and LDA Classifier. Based on


247

the experiment LDA is proved to be the fastest average time at 0.56s compares to GANN. Differ for GANN classifier; the fastest average time is at 30.6s comparing to second fastest time at 30.65s. The slowest average time is 31.02s at 40δ . Based on the experiment LDA gives the faster and synchronizes processing time for all the trials than to the GANN. However LDA gives the lowest accuracy result compare to GANN and vice versa.

VII. CONCLUSION With deeper analysis and adding an interpretation of new pre-processing and classifier, the decision about the pose feature extraction such as GLCM with GANN and LDA can be made. Several examples were conducted to evaluate the performance of the proposed method under different illumination levels. Finally, the classification results indicate that the suggested method could be used as a valuable tool for classifying the infant between normal and pain with GANN.

ACKNOWLEDGMENT This research was conducted under Fundamental Research Grant Scheme (FRGS) which is contributed by Ministry of Higher Education Malaysia.

REFERENCES [1] S. C, Chuang, Shih F. Y., and. Slack M. R. "Machine

recognition and representation of neonatal facial displays of acute pain," Artificial Intelligence in Medicine 36(2), 2006, pp. 211-222.

[2] S. Brahnam, C. Chuang, S. S Randal, and Y. S Frank. "Machine assessment of neonatal facial expressions of acute pain," Decision Support Systems 43, 2007, pp. 1242-125.

[3] S. Brahnam , L. Nanni, and S. Randall. “Introduction to neonatal facial pain detection using common and advanced face classification techniques,” Advanced Computation Intelligence Paradigms in Healthcare, 1, Studies in Computational Intelligence (SCI) Series: Springer-Verlag, Berlin, 48, 2004, pp. 225-253

[4] S. Brahnam, F. C Cgao, Y. S Frank, and R. S Melinda. “SVM

classification of neonatal facial image of pain,“ Proceedings of the 6th International Workshop on Fuzzy Login and Applications (WILF05),

[5] S. Brahnam and L. Nanni , "Neonatal facial pain detection using NNSOA and LSVM," Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV08), Las Vegas, vol. 2, 2008, pp. 352-357,

[6] L. Nanni, S. Brahnam, A. Lumini. "A local approach based on a local binary patterns variant texture descriptor for classifying pain states." Expert Systems with Applications 37(11) pp. 7888-7894

[7] S. Brahnam, C. F. Chuang, F. Y. Shih, and M. R. Slack, "SVM classification of neonatal facial images of pain," Fuzzy Logic and Applications, Isabelle Bloch, Alfredo Petrosino, Andrea G. B. Tettamanzi, editors, Lecture Notes in Computer Science, 3849, pp. 111-115

[8] K. Pun, and Y. Moon, "Recent advances in ear biometrics." pp. 144-149, 2004.

[9] E. H. Land, J. J. McCann. Lightness and retinex theory. Journal of the Optical Society of America, Vol. 61, No. 1, str. 1–11, 1971.

[10] D. J. Jobson, Z. Rahman, G.A. Woodell. Properties and performance of a center/surround retinex. IEEE Transactions on Image Processing, Vol. 6, No. 3, str. 451–462, 1997.

[11] R. M. Haralick, K. Shanmugam, and I. Dinstein, Textural Features of Image Classification, IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-3, no. 6, Nov. 1973

[12] C. Rafael Gonzalez, E. Richard Woods and L. Steven Eddins (2004). Digital Image Processing using MATLAB. Pearson Education. ISBN 978-81-7758-898-9.

[13] B. Schwerin, and K. K. Paliwal, “Local-DCT features for facial recognition”, In Proc. Intern. Conf. Signal Proc. and Communication Systems, Gold Coast, Australia, Dec 2008.

[14] C. Sanderson, 2002, “Automatic Person Verification Using Speech & Face Information”, Dissertation presented to School of Microelectronic Engineering, Griffith University.

[15] A. Fiszelew, P. Britos, A. Ochoa, H. Merlino, E. Fernndez, R. Garca-Martnez, “Finding Optimal Neural Network Architecture Using Genetic Algorithms”. Research in Computing Science 27, pp. 15-24. 2007

[16] M. N. Mansor, S. Yaacob, R. Nagarajan and H. Muthusamy, “Coma Patients Expression Analysis under Different Lighting using k-NN and LDA.” International Journal of Signal and Image Processing (IJSIP), Vol 1, No 4, pp, 249-254, Hyper Sciences, 2010


248

[IEEE 2013 IEEE International Conference on Control System, Computing and Engineering (ICCSCE) -...

Documents

Transcript of [IEEE 2013 IEEE International Conference on Control System, Computing and Engineering (ICCSCE) -...