Journal of Advanced Research in Dynamical and Control Systems Vol. 9. Sp– 17 / 2017
Q-Q PLOT REPRESENTATION OF FEATURE EXTRACTION ALGORITHMS WITH RECENT NETWORK MODELS FOR FACIAL EMOTION RECOGNITION G. Charlyn Pushpa Latha1, SV.Shri Bharathi2 1,2
Assistant Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha University, Thandalam, Chennai, Tamil Nadu. INDIA. Email Id:
[email protected],
[email protected]
ABSTRACT Facial Electromyography is the trending phenomenon for developing an effective emotion recognition system with interface to humans. In this study, analysis of the Facial Electromyography (FEMG) signals are done with respect to the six basic emotions namely anger, disgust, fear, happy, neutral and sad of an individual. Feature extraction algorithms namely bandpower, statistical features and multirate features are used for extracting the most prominent features from the extracted FEMG signals. Comparative analysis of these extracted features are done using the most promising neural network models namely Elman Neural Network (ENN), Feedforward Neural Network (FFNN), Cascade Feedforward Network (CFNN), Layered Recurrent Neural Network (LRN), Fitting Neural Network and the Convolution Neural Network(CNN) models. At the initial stage, 7 subjects took part in the experimental study. For further implementation with the emotion recognition system, 20 people were involved. The Q-Q plot representation was used to check the normality of data and the results of the demonstration proved that most of the representations were not normal. Analyses of the FEMG signals were done with respect to Q-Q plots. Keywords: Q-Q plot, Cascade Neural Network, Convolution Neural Network, Fitting Neural Network, facial electromyography.
1. INTRODUCTION Emotions are very much essential in day to day life of a human and the preliminary research on emotions has led to peculiar real time applications. The face is the vital source of information for revealing the different states of an individual [1]. To accomplish information acquiring and transmission of messages, the facial muscles play a dominant role [2]. A peculiar feature of the facial muscles is the contractions are not voluntary but emotional control [3]. A full-fledged FEMG analysis requires concurrent recording of several facial JARDCS
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muscles. After correct training and motivation, most of the subjects exhibit the emotions namely anger, disgust, fear, happy, neutral and sad within particular frequency bands which can inturn be used as a communication signal. Adaboost classifier with viola jones detector can also be used for identification of humans. The paper is designed as follows: Section2 analyses the obtained FEMG data diagrammatically by way of the Q-Q plot representation. Section3 concludes with the different types of the Q-Q plot representations along with the future work.
2. Q-Q PLOT REPRESENTATION OF FEATURE EXTRACTION ALGORITHMS WITH THE PROPOSED NEURAL NETWORK MODELS Q- Q plots are used for analyzing the distribution of data. These plots are an informal graphical way of the analyzing the fact that the data under study is distributed normally. The graphical method for checking if the data sets under study arise from populations with similar distribution is effected by the quantile – quantile (q-q) plot .Q-Q plots are very useful for comparing the shape of the distribution and it also provides the graphical representation of the properties namely skewness, location and scale which are similar or different in the distributions under study. Q-Q plot gives the plot between the quantiles of the data in the first set and the data in the second set. They are used to check if the data sets under study arise from population with a common distribution and also to check if they have common scale factor and location, similar shapes and if they have a common tail behavior. The advantages of the q-q plot are the sample size involved for q-q plot calculation need not be equal. Also different data distributions can be tested simultaneously. One of the main disadvantages of the q-q plot is that these plots are mainly based on sorting and hence it takes large amount of time when calculating the q-q plot for very large databases. Table 1 demonstrates the calculation of the Q-Q plot parameters for the bandpower feature with the FFNN network for 7 subjects. Parameters namely probability, standardized value and the z- score value are calculated. Figure.1 represents the Q-Q plot for the bandpower and FFNN combination for 7 subjects. The plot between the sample quantities and the theoretical quantities for the band power – FFNN combination is illustrated. From the JARDCS
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figure it is shown that the data distribution of the Q-Q plot for band power and FFNN is normal for some values. The Q-Q plot is left skewed with light tailed pattern since the values are not clustered in a single point but they are spread along the normal line. Table-1: Calculation of Q-Q plot parameters for the bandpower feature with FFNN model for 7 subjects
i
FFNN
prob_in=(i-0.5)/n
z score of i
standardised FFNN
1
89.36
0.07
-1.47
-2.031669977
2
92.32
0.21
-0.79
-0.269585342
3
92.56
0.36
-0.37
-0.126713615
4
93.14
0.50
0.00
0.218559725
5
93.57
0.64
0.37
0.474538237
6
94.14
0.79
0.79
0.813858589
7
94.32
0.93
1.47
0.921012384
Figure 1: Q-Q plot representation for bandpower feature and FFNN for 7 subjects
The calculation of the Q-Q plot parameters for the bandpower and the ENN combination is illustrated in Table 2. The regular parameters like probability function, z score values and the standardized values are calculated and the graph for the Q-Q plot for the sample values and the theoretical values are drawn as shown in Figure.2. From the figure it is evident that the data distribution for the band power and the ENN combination is bimodal in nature since data distribution occurred in both positive as well as negative sides in all the four quadrants.
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i
ENN
prob_in=(i-0.5)/n
z score of i
standardised ENN
1
94.12
0.07
-1.47
0.881317904
2
92.11
0.21
-0.79
0.124288422
3
89.34
0.36
-0.37
-0.918981062
4
90.05
0.50
0.00
-0.651572638
5
94
0.64
0.37
0.836122114
6
94.78
0.79
0.79
1.129894749
7
88.06
0.93
1.47
-1.401069488
Figure 2: Q-Q plot representation for bandpower feature and ENN for 7 subjects
Table 3 shows the Q-Q plot parameter calculation for the statistical features and the FFNN network model. The parameters namely probability value, z score and the standardised FFNN values are computed. The red line drawn across the graph is used to check if the values are distributed parallel along the line. From Figure 3 it is inferred that, the data distribution is said to be heavy tailed since it is clustered towards a particular area. Table-3 : Calculation of Q-Q plot parameters for the statistical features with FFNN model for 20 subjects
i
FFNN
p_i=(i-0.5)/n
z score
standardised FFNN
1
82.54
0.03
-1.96
-2.235491481
2
83.88
0.08
-1.44
-1.717049358
3
85.92
0.13
-1.15
-0.927779261
4
86.92
0.18
-0.93
-0.540882155
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5
86.92
0.23
-0.76
-0.540882155
6
87.5
0.28
-0.60
-0.316481833
7
87.67
0.33
-0.45
-0.250709325
8
87.67
0.38
-0.32
-0.250709325
9
87.67
0.43
-0.19
-0.250709325
10
87.83
0.48
-0.06
-0.188805788
11
88.42
0.53
0.06
0.039463505
12
88.83
0.58
0.19
0.198091318
13
88.92
0.63
0.32
0.232912058
14
89.5
0.68
0.45
0.45731238
15
89.83
0.73
0.60
0.584988425
16
89.92
0.78
0.76
0.619809164
17
90.17
0.83
0.93
0.716533441
18
90.42
0.88
1.15
0.813257718
19
91.33
0.93
1.44
57.16002136
20
94.5
0.98
1.96
60.33002136
Figure 3: Q-Q plot representation for statistical features and FFNN for 20 subjects
The calculation of the Q-Q plot parameters namely probability values, z score values and the standardized values for the statistical features with the ENN model are tabulated in Table 4. Figure 4 represents the Q-Q plot between the sample values and the theoretical values for the statistical features – ENN combination. From the figure it is inferred that, the data distribution is normal for this combination as the distribution of data is parallel to the normality curve represented in red line.
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i
ENN
p_i=(i-0.5)/n
z score
standardised ENN
1
81.79
0.025
-1.96
-0.21532393
2
82.33
0.075
-1.44
-1.686442165
3
83.22
0.125
-1.15
-1.126914289
4
83.67
0.175
-0.93
-0.844006936
5
83.89
0.225
-0.76
-0.705696675
6
84.11
0.275
-0.60
-0.567386413
7
84.44
0.325
-0.45
-0.359921021
8
84.56
0.375
-0.32
-0.28447906
9
84.56
0.425
-0.19
-0.28447906
10
84.67
0.475
-0.06
-0.21532393
11
85.11
0.525
0.06
0.061296593
12
85.44
0.575
0.19
0.268761985
13
85.67
0.625
0.32
0.413359077
14
85.89
0.675
0.45
0.551669338
15
85.89
0.725
0.60
0.551669338
16
86.67
0.775
0.76
1.042042083
17
86.67
0.825
0.93
1.042042083
18
86.89
0.875
1.15
1.180352345
19
87.22
0.925
1.44
1.387817737
20
87.56
0.975
1.96
1.601569959
Figure 4: Q-Q plot representation for statistical features and ENN for 20 subjects
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Table 5 tabulates the Q-Q plot parameters for the statistical features with the CFNN model. The parameters namely probability value, z score values and the standardized values are calculated and a graph is plotted between the sample quantities and the theoretical quantities. The graphical representation is shown in Figure 5. From the figure it is evident that, the distribution of data is normal as the data is distributed parallely along the normality red line. Table -5 : Calculation of Q-Q plot parameters for the statistical features with CFNN model for 20 subjects
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CFNN
p_i=(i-0.5)/n
z score
standardised CFNN
1
80.83
0.025
-1.96
-3.003006847
2
88.83
0.075
-1.44
-0.984857084
3
89.08
0.125
-1.15
-0.921789904
4
89.92
0.175
-0.93
-0.709884179
5
90.17
0.225
-0.76
-0.646816999
6
90.5
0.275
-0.60
-0.563568321
7
90.94
0.325
-0.45
-0.452570084
8
91.5
0.375
-0.32
-0.311299601
9
92.25
0.425
-0.19
-0.122098061
10
93.58
0.475
-0.06
-0.419396748
11
93.67
0.525
0.06
0.236123522
12
94.58
0.575
0.19
0.465688058
13
94.67
0.625
0.32
0.488392243
14
94.83
0.675
0.45
0.528755238
15
95.25
0.725
0.60
0.6347081
16
96.25
0.775
0.76
0.886976821
17
96.58
0.825
0.93
0.970225498
18
96.67
0.875
1.15
0.992929683
19
97
0.925
1.44
1.076178361
20
97.58
0.975
1.96
1.222494219
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Figure 5: Q-Q plot representation for statistical features and CFNN for 20 subjects
The computation of the Q-Q plot parameters for the statistical features with the LRN values are shown in Table 6. The parameters calculated are the standardized LRN values, z score values and the probability values. From figure 6, it is evident that the distribution of data for this Q-Q plot combination is normal in nature as well as right skewed as well as light tailed as the data are parallel to the normality line. Table-6 : Calculation of Q-Q plot parameters for the statistical features with LRN model for 20 subjects
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i
LRN
p_i=(i-0.5)/n
z score
standardised LRN
1
79.92
0.025
-1.96
-1.118970892
2
80.42
0.075
-1.44
-0.989174941
3
80.67
0.125
-1.15
-0.924276966
4
82.17
0.175
-0.93
-0.534889113
5
82.25
0.225
-0.76
-1.075229657
6
82.42
0.275
-0.60
-0.469991138
7
83
0.325
-0.45
-0.319427835
8
83.17
0.375
-0.32
-0.275297212
9
83.25
0.425
-0.19
-0.25452986
10
83.42
0.475
-0.06
-0.210399236
11
83.58
0.525
0.06
-0.168864532
12
83.58
0.575
0.19
-0.168864532
13
83.92
0.625
0.32
-0.080603285
14
84
0.675
0.45
-0.059835933
15
85.5
0.725
0.60
0.329551919
16
85.67
0.775
0.76
0.373682542
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85.75
0.825
0.93
0.394449895
18
85.92
0.875
1.15
0.438580518
19
87.67
0.925
1.44
0.892866346
20
98.33
0.975
1.96
3.660116018
Figure 6: Q-Q plot representation for statistical features and LRN for 20 subjects
The Q-Q plot parameter calculation for the Multidownsample features with the CFNN model are shown in Table 2.7. The parameters computed are namely z score values, standardized CFNN values and the probability values. From figure 2.7 it is evident that, the data distribution is normal for these values as the data are distributed parallely along the normality curve. Table-7 : Calculation of Q-Q plot parameters for the multidownsample features with CFNN model for 20 subjects
i
CFNN
p_i=(i-0.5)/n
z score
standardised CFNN
1
86.42
0.025
-1.96
-1.98638818
2
88.33
0.075
-1.44
-1.140362665
3
88.5
0.125
-1.15
-1.065061964
4
88.92
0.175
-0.93
-0.87902494
5
89
0.225
-0.76
-0.843589316
6
89.08
0.275
-0.60
-1.054730917
7
89.5
0.325
-0.45
-0.622116668
8
89.75
0.375
-0.32
-0.511380344
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9
90.42
0.425
-0.19
-0.388315376
10
90.75
0.475
-0.06
-0.068435048
11
90.83
0.525
0.06
-0.032999425
12
90.83
0.575
0.19
-0.032999425
13
91.58
0.625
0.32
0.299209548
14
92.42
0.675
0.45
0.671283596
15
92.92
0.725
0.60
0.892756244
16
93.17
0.775
0.76
1.003492568
17
93.25
0.825
0.93
1.038928192
18
93.42
0.875
1.15
1.114228892
19
94.17
0.925
1.44
1.446437864
20
94.83
0.975
1.96
1.73878176
Figure7: Q-Q plot representation for multidownsample features and CFNN for 20 subjects
The computation of the Q-Q plot parameters for the Multidownsample features with the fitting model is demonstrated in Table 8. The parameters computed are the z score values, probability values and the standardized fitting values. A graph is plotted between the sample values and the theoretical values and the graphical illustration is depicted in Figure 8. From the figure it is evident that, the data distribution is bimodal in nature. Table -8 : Calculation of Q-Q plot parameters for the multidownsample features with Fitting model for 20 subjects
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i
Fitting
p_i=(i-0.5)/n
z score
standardised Fitting
1
78.56
0.025
-1.96
-1.561489711
2
80
0.075
-1.44
-1.359227844
3
80.5
0.125
-1.15
-1.288998028
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4
81.42
0.175
-0.93
-1.159775168
5
81.83
0.225
-0.76
-1.10218672
6
83.75
0.275
-0.60
-0.83250423
7
84.67
0.325
-0.45
-0.70328137
8
87.42
0.375
-0.32
-0.317017386
9
88.83
0.425
-0.19
-0.118969307
10
89.58
0.475
-0.06
-0.013624584
11
90.25
0.525
0.06
0.080483368
12
90.67
0.575
0.19
0.139476413
13
91.92
0.625
0.32
0.315050951
14
96.42
0.675
0.45
0
15
96.91
0.725
0.60
1.015944507
16
97.08
0.775
0.76
1.039822644
17
98.06
0.825
0.93
1.177473082
18
98.34
0.875
1.15
1.216801778
19
98.61
0.925
1.44
1.254725879
20
98.72
0.975
1.96
1.270176438
Figure 8: Q-Q plot representation for multidownsample features and Fitting for 20 subjects
The Q-Q plot parameters calculation for the multiupfirdn features with the CFNN model is illustrated in Table 9. Parameters namely z score, standardized CFNN as well as the probability values are calculated and the graph is plotted as shown in Figure 9. From the figure it is evident that the data distribution is bimodal in nature as the data is distributed on either side of the normality curve.
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i
CFNN
p_i=(i-0.5)/n
z score
standardised CFNN
1
89.25
0.025
-1.96
-1.799753629
2
89.58
0.075
-1.44
-1.634362736
3
90.08
0.125
-1.15
-1.383770473
4
90.58
0.175
-0.93
-1.133178211
5
91.33
0.225
-0.76
-0.757289817
6
91.67
0.275
-0.60
-0.586887079
7
92.25
0.325
-0.45
-0.296200054
8
92.33
0.375
-0.32
-0.256105292
9
92.5
0.425
-0.19
0
10
93
0.475
-0.06
0.079688339
11
93.08
0.525
0.06
0.119783101
12
93.5
0.575
0.19
0.330280602
13
93.5
0.625
0.32
0.330280602
14
94.08
0.675
0.45
0.620967626
15
94.17
0.725
0.60
0.666074234
16
94.42
0.775
0.76
0.791370365
17
94.42
0.825
0.93
0.791370365
18
94.75
0.875
1.15
0.956761258
19
96.08
0.925
1.44
1.623336676
20
96.25
0.975
1.96
1.708538046
Figure 9: Q-Q plot representation for multiupfirdn features and CFNN model for 20 subjects
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For the combination multiupfirdn – Fitting model, the Q-Q plot parameters calculated are the z score values, probability values and the standardized Fitting values. The tabulation is done in Table 10. The Q-Q plot data distribution for the sample quantities as well as the theoretical quantities is is demonstrated in Figure 10. From the figure it is understood that, the data distribution for this combination is heavy tailed in nature as the data is clustered within a particular area. Table-10: Calculation of Q-Q plot parameters for the multiupfirdn features with Fitting model for 20 subjects
i
Fitting
p_i=(i-0.5)/n
z score
standardised Fitting
1
89.67
0.025
-1.96
-1.557912154
2
89.75
0.075
-1.44
-1.515613504
3
90.08
0.125
-1.15
-1.341131573
4
90.75
0.175
-0.93
-0.986880379
5
90.92
0.225
-0.76
-0.896995747
6
91.17
0.275
-0.60
-0.764812466
7
91.5
0.325
-0.45
-0.590330535
8
92
0.375
-0.32
-0.325963972
9
92.33
0.425
-0.19
-0.15148204
10
92.58
0.475
-0.06
-0.019298759
11
93
0.525
0.06
0.202769154
12
93.08
0.575
0.19
0.245067804
13
93.58
0.625
0.32
0.509434366
14
93.83
0.675
0.45
0.641617648
15
94.08
0.725
0.60
0.773800929
16
94.17
0.775
0.76
0.821386911
17
94.17
0.825
0.93
45.20058848
18
94.25
0.875
1.15
0.863685561
19
94.5
0.925
1.44
0.995868842
20
96.92
0.975
1.96
2.275403006
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Figure 10: Q-Q plot representation for multiupfirdn features and Fitting model for 20 subjects
The Q-Q plot parameter calculation for the multidecimate features and the Fitting model are tabulated in Table 11. The parameters suitable for calculation are the z score values, probability values and the standardized CFNN values. Figure 11 depicts the Q-Q plot for the multi decimate and the Fitting network combination. From the figure it is evident that, the data distribution is bimodal in nature as the data are distributed on either side of the normality curve. Table -11: Calculation of Q-Q plot parameters for the multidecimate features with CFNN model for 20 subjects
i
CFNN
p_i=(i-0.5)/n
z score
standardised CFNN
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1
83.78
0.025
-1.96
-1.810836401
2
83.84
0.075
-1.44
-1.792126192
3
86.22
0.125
-1.15
-1.049954566
4
86.78
0.175
-0.93
-0.875325948
5
86.89
0.225
-0.76
-0.841023898
6
87.83
0.275
-0.60
-0.547897289
7
88
0.325
-0.45
-0.49488503
8
88.83
0.375
-0.32
-0.236060471
9
88.83
0.425
-0.19
-0.236060471
10
89.17
0.475
-0.06
-0.130035953
11
89.28
0.525
0.06
-0.095733903
12
90.39
0.575
0.19
-0.174212836
13
90.39
0.625
0.32
-0.174212836
14
91.39
0.675
0.45
0.562241783
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15
92.06
0.725
0.60
-0.315846148
16
92.17
0.775
0.76
0.8054745
17
93.67
0.825
0.93
1.273229727
18
93.72
0.875
1.15
1.288821568
19
93.72
0.925
1.44
1.288821568
20
94.78
0.975
1.96
1.619368595
Figure 11: Q-Q plot representation for multi decimate features and CFNN model for 20 subjects
Table 12 tabulates the Q-Q plot parameters for the multi decimate features and the Fitting model. Parameters namely probability values, z score values and the standardized fitting values for the multi decimate- fitting combination are tabulated. The corresponding Q-Q plot is demonstrated in Figure 12. From the figure it is analyzed that the data is distributed bimodally. Table-12 : Calculation of Q-Q plot parameters for the multidecimate features with Fitting model for 20 subjects
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i
Fitting
p_i=(i-0.5)/n
z score
standardised Fitting
1
87.62
0.025
-1.96
-2.680975421
2
89.42
0.075
-1.44
-1.916983375
3
91.42
0.125
-1.15
-1.068103324
4
92.5
0.175
-0.93
-0.609708097
5
93.25
0.225
-0.76
-0.291378078
6
93.33
0.275
-0.60
-0.257422875
7
93.5
0.325
-0.45
-0.185268071
8
93.58
0.375
-0.32
-0.151312869
9
93.67
0.425
-0.19
-0.113113267
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10
94.42
0.475
-0.06
0.205216752
11
94.5
0.525
0.06
0.239171954
12
94.67
0.575
0.19
0.311326759
13
95
0.625
0.32
0.451391967
14
95
0.675
0.45
0.451391967
15
95.17
0.725
0.60
0.523546771
16
95.42
0.775
0.76
0.629656778
17
95.92
0.825
0.93
0.841876791
18
96.42
0.875
1.15
1.054096803
19
96.75
0.925
1.44
1.194162012
20
97.17
0.975
1.96
1.372426823
Figure 12: Q-Q plot representation for multidecimate features and Fitting model for 20 subjects
The Q-Q plot parameters for the Multidownsample features with the CNN model is tabulated in Table 13 . Parameters namely standardized CNN, z score as well as the probability values are calculated. Figure 13 demonstrates the Q-Q plot representation of the Multidownsample and CNN combination. From the figure it is true that the data distribution is bimodal in nature. Table-13 : Calculation of Q-Q plot parameters for the multidownsample features with CNN model for 20 subjects
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i
CNN
p_i=(i-0.5)/n
z score
Standardised CNN
1
99.68
0.025
-1.96
-1.333843382
2
99.69
0.075
-1.44
-0.97334517
3
99.69
0.125
-1.15
-0.97334517
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4
99.69
0.175
-0.93
-0.97334517
5
99.69
0.225
-0.76
-0.97334517
6
99.7
0.275
-0.60
-0.612846959
7
99.7
0.325
-0.45
-0.612846959
8
99.7
0.375
-0.32
-0.612846959
9
99.7
0.425
-0.19
-0.612846959
10
99.71
0.475
-0.06
-0.252348748
11
99.71
0.525
0.06
-0.252348748
12
99.71
0.575
0.19
-0.252348748
13
99.72
0.625
0.32
0.108149463
14
99.73
0.675
0.45
0.340470533
15
99.74
0.725
0.60
0.829145886
16
99.74
0.775
0.76
0.829145886
17
99.75
0.825
0.93
1.189644097
18
99.75
0.875
1.15
1.189644097
19
99.77
0.925
1.44
1.91064052
20
99.77
0.975
1.96
1.91064052
Figure 13: Q-Q plot representation for multidownsample features and CNN model for 20 subjects
Table 2.14 tabulates the Q-Q plot parameters for the upfirdn features with the CNN model namely z score, probability values and the standardized CNN values. Figure 2.14 demonstrates the Q-Q plot for the Multidownsample – CNN combination. From the figure it is true that the data distribution is bimodal in nature.
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i
CNN
p_i=(i-0.5)/n
z score
Standardised CNN
1
99.69
0.025
-1.96
-1.115771414
2
99.69
0.075
-1.44
-1.115771414
3
99.7
0.125
-1.15
-0.777658864
4
99.7
0.175
-0.93
-0.777658864
5
99.7
0.225
-0.76
-0.777658864
6
99.71
0.275
-0.60
-0.439546315
7
99.71
0.325
-0.45
-0.439546315
8
99.71
0.375
-0.32
-0.439546315
9
99.71
0.425
-0.19
-0.439546315
10
99.71
0.475
-0.06
-0.439546315
11
99.71
0.525
0.06
-0.439546315
12
99.71
0.575
0.19
-0.439546315
13
99.72
0.625
0.32
-0.101433765
14
99.73
0.675
0.45
0.236678785
15
99.73
0.725
0.60
0.236678785
16
99.74
0.775
0.76
0.574791335
17
99.74
0.825
0.93
0.574791335
18
99.78
0.875
1.15
1.927241533
19
99.78
0.925
1.44
1.927241533
20
99.79
0.975
1.96
2.265354083
Figure 14 Q-Q plot representation for multiupfirdn features and CNN model for 20 subjects
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The calculation of the Q-Q plot parameters for the multi decimate features with the CNN model is shown in Table 15. Parameters namely z score, probability values as well as the standardized values are computed. Figure 15 illustrates the Q-Q plot graphical representation for the multi decimate- CNN combination. From the figure it is evident that, the data distribution is partially bimodal as well as heavy tailed in nature. Table-15 : Calculation of Q-Q plot parameters for the multidecimate features with CNN model for 20 subjects
i
CNN
p_i=(i-0.5)/n
z score
Standardised CNN
1
99.67
0.025
-1.96
-1.916372412
2
99.69
0.075
-1.44
-1.117883907
3
99.69
0.125
-1.15
-1.117883907
4
99.7
0.175
-0.93
-0.718639655
5
99.7
0.225
-0.76
-0.718639655
6
99.7
0.275
-0.60
-0.718639655
7
99.71
0.325
-0.45
-0.319395402
8
99.71
0.375
-0.32
-0.319395402
9
99.71
0.425
-0.19
-0.319395402
10
99.71
0.475
-0.06
-0.319395402
11
99.72
0.525
0.06
0.07984885
12
99.72
0.575
0.19
0.07984885
13
99.72
0.625
0.32
0.07984885
14
99.72
0.675
0.45
0.07984885
15
99.73
0.725
0.60
0.07984885
16
99.73
0.775
0.76
0.07984885
17
99.75
0.825
0.93
1.277581608
18
99.75
0.875
1.15
1.277581608
19
99.76
0.925
1.44
1.676825861
20
99.77
0.975
1.96
2.076070113
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Figure 15: Q-Q plot representation for multi decimate features and CNN model for 20 subjects
3. CONCLUSION Identifying the person’s emotional state through the FEMG signal has drawn increasing attention. FEMG recording may be considered a sensitive technique for inferring subjective mood states or affective responses. However, it has limitations for many applications under natural life circumstances due to its obtrusiveness and the fact that facial activity is influence by many other, nonaffective, behavioural factors. In this research, mutirate features are proposed to recognize the six facial emotions namely anger, disgust, fear, happy, neutral and sad using the neural network models. In order to facilitate and analyse the FEMG data distribution, Q-Q plot was used. The Q-Q plot representation was used to check the normality of data and the results of the demonstration proved that most of the representations were not normal. Real time experimentation was missing in the previous work. But our proposed system worked well for offline analysis and it could be further interfaced to real time system for proper functioning.
REFERENCES 1. Anton Van Boxtel,”Facial EMG as a Tool for Inferring Affective
States”,
Proceedings of Measuring Behaviour 2010 (Eindhoven, The Netherlands, August 24-27,2010), Tilburg University 2. Cheng- Ning Huang, Chun-Han Chen, Hung-Yuan Chung, ”The Review
of
Applications and the Measurements in the Facial Electromyography”, Journal of Medical and Biological
Engineering
25(I):15- 20, Department of
Electrical
Engineering, National Central University, Chung-Li, Taiwan, 320 ROC
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Journal of Advanced Research in Dynamical and Control Systems Vol. 9. Sp– 17 / 2017
3. Lapatki, B.G, Stegeman, D.F and Jonas, I.E, “A surface EMG electrode for the simultaneous observation of multiple facial muscles,” Journal of Neuroscience Methods, 123(2) : 117-128, 2003 4. G. Rigas, C. D. Katsis, G. Ganiatsas and D. I. Fotiadis,” A User Independent, Biosignal Based Emotion Recognition Method,” Proceedings of the11th International Conference, UM 2007, Corfu,
Greece, July 25-29, 2007, pp:314- 318,2007
5. Wee Ming Wong, Ayer Keroh, Malaysia Tan, A. W. C ; Chu Kiong Loo ; Wei Shiung
Liew,” PSO optimization of Synergetic Neural classifier for multichannel
emotion recognition”, Second World
Congress on Nature and Biologically
Inspired Computing (NaBIC),pp.316-321,15-17, Dec,2010. 6. Shanxiao Yang, Guangying Yang,” Emotion Recognition of EMG Based on Improved L-M BP Neural Network and SVM,” Journal of Software, Vol 6, No 8, pp. 1529-1536, Aug 2011. 7. Hamedi, M. Tehran, Iran Rezazadeh, I.M; Firoozabadi “Facial Gesture recognition using two- channel bio- Sensors configuration and fuzzy classifier: A pilot study,” M. Electrical, Control and
Computer Engineering (INECCE), International
Conference 21- 22, Page(s): 338 – 343, June 2011 8.
Jonghwa, K and E. Ande, 2008, ”Emotion Recognition Based on
Physiological
Changes in Music Listening,” IEEE Transactions on Pattern Analysis and Machine Intelligence 30(12):2067-2083. 9. Guillaume Gibert, Martin Pruzinec, Tanja Schultz, Catherine Stevens,” Enhancement of Human Computer Interaction with facial Electromyographic sensors”, OZCHI 2009, November 23-27, 2009, Melbourne, Australia,pp:421-424 10. Eun-Hye Jang, Byoung-Jun Park, Sang-Hyeob Kim, Myoung-Ae Chung, Mi-Sook Park, Jin-Hun Sohn,” Classification of Human Emotions from Physiological signals using Machine Learning Algorithms “,
ACHI 2013 : The Sixth International
Conference on Advances in Computer-Human Interactions, Copyright (c) IARIA, 2013. ISBN: 978-1-61208-250-9,pp:395-400.
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