Computational Intelligence - August 2015 - 48

z Axis

1
0.3
0
0

1
0.6471
2

4

x Axis

6

h Axis

8

10

0

(a)

h Axis

1

0.6471

0

0

2

4

6

8

10

x Axis
(b)
Figure 4 (a) A 3-dimensional surface computed from the h-secondary membership functions of the INs in Fig. 2(a), truncated (the surface) by a plane through z = 0.3 parallel to the x - h plane. (b) The
two primary membership functions on the zSlice of Fig. 4(a) (for
z = 0.3) .

per subject, one per different facial expression or configuration: center-light, w/glasses, happy, left-light, w/no
glasses, normal, right-light, sad, sleepy, surprised, and
wink. In order to remove irrelevant image content, the
images were preprocessed by the Viola-Jones face detector
followed by ellipse masking as described in [16]. The
resulting localized faces were cropped to a fixed size of
32 # 32 pixels each.
2) TERRAVIC dataset [11]: It regards infrared face recognition. It contains 24, 508 images of 20 persons under different conditions such as front, left and right poses,
indoor/outdoor environments with glasses and/or hat
accessories; each image has an 8-bit, 320 # 240 pixels size.

We used 70 images per person for the first 10 persons
(i.e., classes) as described in [26].
3) JAFFE dataset [18]: It regards facial expression recognition. It contains 213 frontal images (256 # 256 pixels
each) of 10 different persons corresponding to 7 common
human facial expressions (i.e., classes), namely neutral
(30), angry (30), disgusted (29), fear (32), happy (31), sad
(31), and surprise (30) regarding Japanese female subjects,
where a number within parentheses indicates the number
of images available per facial expression. In order to
remove irrelevant image content, the images were preprocessed as in the YALE dataset above. In conclusion, face
images of 160 # 160 pixels each were produced.
4) TRIESCH I dataset [28]: It regards hand posture recognition. It contains 8-bit images (128 # 128 pixels each) of
10 hand postures (i.e., classes) regarding 24 persons in
dark background.
It is understood that none of the above mentioned datasets
is "big (data)"; nevertheless, any of the above datasets is big
enough for the objectives here, where a large number of experiments were carried out toward comparing various classifiers.
We extracted six types of features per image. More specifically, we computed four different families of orthogonal
moments including Zernike (ZMs), Gaussian-Hermite
(GHMs), Tchebichef (TMs) and dual Hahn (dHMs) moments
[23]; the order of each moment family was selected such that a
16-dimensional vector was produced. Another two types of
features, popular in face recognition applications, were extracted, namely the Local Binary Pattern (LBP) and Histogram of
Oriented Gradient (HOG) [24]. Regarding LBP, uniform patterns of (R, N ) = (1, 8) regions were computed. The vector
length for LBP and HOG was 59 and 9, respectively.
Table 1 summarizes the characteristics of the image datasets
used in our 10-fold cross-validation experiments. More specifically, the first column in Table 1 indicates the type of (image) feature that produced the best classification results for a dataset as
well as the corresponding number of input features. For instance,
for the YALE dataset, the best classification results were obtained
for the 59 LBP input features, etc. The remaining columns in
Table 1 display the number of instances (i.e., the total number of
images used), the number of training data (per fold), the number
of testing data (per fold), and the number of classes.
B. Experimental Setup

We employed ten traditional classifiers including three versions of the Minimum Distance Classifier (MDC) corresponding to the Chi Square ( | 2), Euclidean and Manhattan
distances, respectively [26] - An
MDC classif ier here engaged
Table 1 Characteristics of the image datasets used in 10-fold cross-validation experiments.
"mean feature vectors" [24]. In
FeaTure TYPe
#TraiNiNg DaTa #TeSTiNg DaTa
addition, we employed a kNN
DaTaSeT NaMe (#FeaTureS)
#iNSTaNCeS Per FOlD
Per FOlD
#ClaSSeS
(k =1), a Na¨ıve-Bayes, an RBF
YALE
LBP (59)
165
149
16
15
ELM,
a three-layer feedforward
TERRAVIC
ZMs (16)
700
630
70
10
JAFFE
dHMs (16)
213
192
21
7
backpropagation Neural Network,
TRIESCH I
HOG ( 9)
240
216
24
10
and three types of Support Vector

48

IEEE ComputatIonal IntEllIgEnCE magazInE | august 2015



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