Facial Emotion Recognition using Min-Max
Similarity Classifier
Olga Krestinskaya and Alex Pappachen James

arXiv:1801.00451v1 [] 1 Jan 2018

School of Engineering, Nazarbayev University, Astana
www.biomicrosystems.info/alex
Email: apj@ieee.org

Abstract—Recognition of human emotions from the imaging
templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic
recognition of facial expressions using image template matching
techniques suffer from the natural variability with facial features
and recording conditions. In spite of the progress achieved
in facial emotion recognition in recent years, the effective
and computationally simple feature selection and classification
technique for emotion recognition is still an open problem. In
this paper, we propose an efficient and straightforward facial
emotion recognition algorithm to reduce the problem of interclass pixel mismatch during classification. The proposed method
includes the application of pixel normalization to remove intensity
offsets followed-up with a Min-Max metric in a nearest neighbor
classifier that is capable of suppressing feature outliers. The
results indicate an improvement of recognition performance
from 92.85% to 98.57% for the proposed Min-Max classification
method when tested on JAFFE database. The proposed emotion
recognition technique outperforms the existing template matching methods.
Index Terms—Face emotions, Classifier, Emotion recognition,
spatial filters, gradients

I. I NTRODUCTION
In recent years, the human-computer interaction challenge
has led to the demand to introduce efficient facial and speech
recognition systems [1]–[5]. Facial emotion recognition is
the identification of a human emotion based on the facial
expression and mimics [6]. The facial emotion recognition has
a wide range of appliction prospects in different areas, such as
medicine [7], robotics [8], [9], computer vision, surveillance
systems [1], machine learning [10], artificial intelligence,
communication [11], [12], psychological studies [4], smart
vehicles [9], security and embedded systems [13].
There are two main approaches for facial expression recognition: geometry-based and appearance-based methods [2].
The geometry-based methods extract main feature points and
their shapes from the face image and calculate the distances
between them. While, appearance-based methods focus on
the face texture using different classification and template
matching methods [14], [15]. In this paper, we focus on facial
emotion recognition based on template matching techniques
that remains a challenging task [16]–[18]. Since the orientation
of pixel features are sensitive to the changes in illumination, pose, scale and other natural imaging variabilities, the
matching errors tend to be high [4], [19], [20]. Pixel matching
methods are known to be useful when the images has missing

features because imaging matrices become sparse and feature
computation process is not trivial. As facial expressions cause
a mismatch of intra-class features due to their orientation
variability, it is difficult to map them between the imaging
templates.
Facial emotion recognition accuracy depends on the robustness of a feature extraction method to intra-class variations and
classifier performance under noisy conditions and with various
types of occlusions [10]. Even thought a variety of approaches
for the automated recognition of human expressions from
the face images using template matching methods have been
investigated and proposed over the last few years [14], the
emotion recognition method with robust feature extraction
and effective classification techniques accompanied by low
computational complexity is still an open research problem
[21]. Therefore, in this paper, we address the issues of
matching templates through pixel normalization followed by
the removal of inter-image feature outliers using a Min-Max
similarity metric. We apply Gaussian normalization method
with local mean and standard deviation to normalize the
pixels and extract relevant face features followed by MinMax classification method to determine an emotion class. The
simulation is performed in Matlab for the Japanese Female
Facial Expression (JAFFE) database [22] and the emotion
recognition accuracy is calculated using leave-one-out crossvalidation method.
The main contributions of this work are the following:
•

•

•

We develop a simplified approach for facial emotion
recognition with template matching method using Gaussian normalization, mean and standard deviation based
feature extraction and Min-Max classification approach.
We present simple and effective facial emotion recognition algorithm having low computational complexity
and ability to suppress the outliers and remove intensity
offsets.
We conduct the experiments and simulations on JAFFE
database to demonstrate the efficiency of the proposed
approach and highlight its advantages, comparing to the
other existing methods.

The paper is organized as follows. Section II presents the
overview of the existing methods for facial emotion recognition, their drawbacks and reasons to propose a new method.
In Section III, we show normalization, feature extraction

and classification parts of the proposed method, present the
algorithm and describe the experiments. Section IV contains
the simulation results and comparison of the obtained results
with the existing methods. In Section V, we discuss the
benefits and drawbacks of the proposed method, in comparison
to the traditional methods. Section VI concludes the paper.
II. BACKGROUND AND RELATED WORKS
To address the problem of facial emotion recognition, several template matching methods have been proposed in the last
decades [1], [8], [23]–[25]. In most of the cases, the process of
emotion recognition from human face images is divided into
two main stages: feature extraction and classification [1], [8].
The main aim of feature extraction methods is to minimize
intra-class variations and maximize inter-class variations. The
most important facial elements for human emotion recognition
are eyes, eyebrows, nose, mouth and skin texture. Therefore, a
vast majority of feature extraction methods focus on these features [2], [26]. The selection of irrelevant face image features
or insufficient number of them would lead to low emotion
recognition accuracy, even applying effective classification
methods [21]. The main purpose of the classification part is
to differentiate the elements of different emotion classes to
enhance emotion recognition accuracy.
The commonly used feature extraction methods include twodimensional Linear Discriminant Analysis (2D-LDA) [8], [25],
two-dimensional Principle Component Analysis (2D-PCA)
[27], Discrete Wavelet Transform (DWT) [6], [8], [28], Gabor
based methods [29], [30] and wavelets-based techniques [23],
[31]. In 2D-LDA method, the two-dimensional image matrix
is exploited to form scatter matrices between the classes and
within the class [8]. 2D-LDA method can be applied for facial
features extraction alone or accompanied with the other feature
extraction method, as DWT [8], [25]. In 2D-PCA feature
extraction method, the covariance matrix representation of the
image is derived directly from the original image [8], [27]. The
size of the derived principle component matrix is smaller than
the original image size that allows to decrease the amount
of processing data, and consequently, reduce the required
computational memory [32]. However, 2D-LDA and 2D-PCA
methods applied in template matching techniques require an
additional processing of the image, dimensionality reduction
techniques or application of another feature extraction method
to achieve higher recognition accuracy, which leads to the
increase in processing time.
The other feature extraction method is DWT. This method
is based on the low-pass and high-pass filtering, therefore,
it is appropriate for the images with different resolution
levels [8]. In the emotion recognition task, DWT is applied
for the extraction of useful features from the face images
and can be replaced with its Orthogonal Wavelet Transform
(OWT) and Biorthogonal Wavelet Transform (BWT) having
the advantages of orthogonality [6]. Another method for facial emotion recognition is Gauss-Laguerre wavelet geometry
based technique. This method represents the processed image
in polar coordinates with the center at a particular pivot point.

The degree of freedom is one of the advantages that GaussLaguerre approach provides, which in turn allows to extract of
features of the desirable frequency from the images [23], [31].
However, DWT and Gauss-Laguerre approaches are complex
and require time and memory consuming calculations.
The classification of the extracted features can be implemented using Support Vector Machine (SVM) algorithm [8],
[28], K-Nearest Neighbor (KNN) method [23], [33], Random
Forest classification method [7], [34] and Gaussian process
[24]. The SVM principle is based on non-linear mapping and
identification of a hyperplane for the separation of data classes.
SVM classifier is used with the application of different kernel
functions, such as linear, quadratic, polynomial and radial
basis functions, to optimize the SVM performance [8], [28].
KNN approach is based on the numerical comparison of a
testing data sample with training data samples of each class
followed by the determination of the similarity scores. The
data class is defined by K most similar data samples based
on the minimum difference between train and test data [23],
[33]. KNN and SVM classifiers are simple and widely used for
emotion recognition, however these classifiers do not suppress
the outliers that leads to lower recognition accuracy.
The Random Forest classification method is based on the decision making tree approach with randomized parameters [7],
[34]. To construct the decision tree, Random Forest algorithm
takes a random set of options and selects the most suitable
from them [35]. Random Forest classifier is robust and has a
high recognition rate for the images of large resolution [36].
The drawback of Random Forest classifier is its computational
complexity. The other classification method is the Gaussian
process approach. Gaussian process is based on the predicted
probabilities and can be used for facial emotion recognition without application of feature selection algorithms. The
Gaussian process allows a simplified computational approach,
however has a smaller emotion recognition rate, comparing to
the other methods [24].
Even thought the a number of methods for feature extraction
and classification have been proposed, there is a lack of template matching methods that allow to achieve high recognition
accuracy with minimum computational cost. Therefore, the
method that we propose has a potential to reduce the computational complexity of facial emotion recognition operation and
increase the recognition accuracy due to the simplicity of the
algorithm, effective feature extraction and ability to suppress
outliers. The proposed algorithm can be implemented using
small computational capacity devices keeping facial emotion
recognition operation fast and accurate.
III. M ETHODOLOGY
The main idea of the proposed method is to extract the
spatial change of standardized pixels in a face image and
detect the emotion class of the face using a Min-Max similarity Nearest Neighbor classier. The images from the JAFFE
database [22] are used for the experiments. This database
contains 213 images of 10 female faces comprising 6 basic
facial expressions and neutral faces. The original images from

the database have the size of 256 × 256 pixels and in our
experiments they are cropped to a size of 101 × 114 pixels
retaining only the relevant information of the face area. A
block diagram of the proposed method is shown in Fig. 1.

JAFFE database does not contain the face images with different illumination conditions, the illumination change was
created by adding and subtracting the value of 10 from the
original image. Fig. 2 (b) illustrates the respective images after
Gaussian local normalization. Irrespective of the illumination
conditions, the three locally normalized images appear similarly with the minimum pixel intensity variation.

(b)

(a)

(c)
Fig. 2. (a) Sample image from JAFFE database with different lighting
conditions obtained by adding and subtracting a value of 10 from the original
image.(b) Normalized images of above sample images obtained by performing
Gaussian normalization using local mean and local standard deviation taken
over a window of size N=11. (c) Feature detected images from the normalized
image by performing local standard deviation using a window of size M=11.

Fig. 1. Outline of the proposed emotion recognition system

B. Feature detection
A. Pre-processing
Illumination variability introduces the inter-class feature
mismatch resulting in the inaccuracies in the detection of
emotion discriminating features from the face images. Therefore, image normalization is essential to reduce the inter-class
feature mismatch that can be viewed as intensity offsets. Since
the intensity offsets are uniform within a local region, we
perform Gaussian normalization using local mean and standard
deviation. The input image is represented as x(i, j), and y(i, j)
is the normalized output image, where i and j are the row and
column number of the processed image. The normalized output
image is calculated by Eq. 1 [37], where µ is a local mean
and σ is a local standard deviation computed over a window
of N × N size.

The feature parts useful for the facial emotion recognition
are eyes, eyebrows, cheeks and mouth regions. In this experiment, we perform the feature detection by calculating local
standard deviation of normalized image using a window of
M×M size. Eq. 4 is applied for the feature detection with
b = (M − 1)/2.
v
u
b
b
X
u 1 X
[y(k + i, h + j) − µ0 (i, j)]2 (4)
w(i, j) = t 2
M
k=−b h=−b

In Eq. 4 the parameter µ0 refers to the mean of the
normalized image y(i, j) and can be calculated by Eq. 5.
b
b
1 X X
µ (i, j) = 2
y(k + i, h + j)
M
0

y(i, j) =

x(i, j) − µ(i, j)
6σ(i, j)

(1)

The parameters µ and σ are calculated using Eq. 2 and 3
with a = (N − 1)/2.

µ(i, j) =

a
a
1 X X
x(k + i, h + j)
N2

(2)

k=−a h=−a

v
u
a
a
X
u 1 X
σ(i, j) = t 2
[x(k + i, h + j) − µ(i, j)]2 (3)
N
k=−a h=−a

An example image from the JAFFE database with three
different lighting conditions is shown in Fig. 2 (a). As the

(5)

k=−b h=−b

Fig. 2 (c) shows the results of feature detection corresponding to the normalized images.
C. Emotion Classification
For the recognition stage, we propose a Min-Max similarity metric in a Nearest Neighbor classifier framework. This
method is based on the principle that the ratio of the minimum
difference to the maximum difference of two pixels will
produce a unity output for equal pixels and an output less than
unity for unequal pixels. The proposed method is described in
Algorithm 1. The algorithm parameter trainlen refers to the
number of train images, N corresponds to the normalization
window size, and M indicates the feature detection window
size. Each cropped image is of m × n pixel dimension. The

parameter train is a feature array of trainlen×(m × n) size,
where each row corresponds to the processed train images.
After normalization and feature detection, test images are
stored in to a vector test of 1×(m × n) size. A single test
image is compared pixel-wise with processed train images of
all the classes in the feature array using the proposed Min-Max
classifier:
s(i, j) = [

min[train(i, j), test(1, j)] α
] ,
max[train(i, j), test(1, j)]

(6)

where a parameter α controls the power of exponential to
suppress the outlier similarities. Outliers are the observations
that come inconsistent with the remaining observations and
are common in real-time image processing. The presence of
outliers may cause misclassification of an emotion, since sample maximum and sample minimum are maximally sensitive
to them. In order to remove the effects of the outliers, α = 3 is
selected to introduce the maximum difference to the inter-class
images and minimum difference to the intra-class images.
After Min-Max classification, a column vector z of
trainlen × 1 size containing the weights obtained after comparing the test image with each of the trainlen number of train
images is calculated using Eq. 7.

z(i) =

m×n
X

s(i, j)

IV. R ESULTS AND COMPARISON
To benchmark the performance of the proposed algorithm,
leave-one-out cross-validation method has been used. In this
method, one image of each expression of each person is
applied for testing and the remaining images are used for
training [23]. The cross-validation is repeated 30 times to
obtain a statistically stable performance of the recognition
system and to ensure that all the images in JAFFE database are
used for testing at least once. The overall emotion recognition
accuracy of the system is obtained by averaging the results of
the cross-validation trials. Fig. 3 shows the different accuracy
rates obtained for each trial by varying feature detection
window size M from 3 to 21 and keeping normalization
window size at N = 11. It is shown that the maximum emotion
recognition accuracy this normalization window size can be
achieved with the detection window size of 11.

(7)

j=1

The classification output out shown in Eq. 8 is the maximum
value of z corresponding to the train image that shows the
maximum match. The recognized emotion class is the class of
the matched train image.
out = max(z(i))

(8)

Fig. 3. The accuracy rates obtained for four trials of leave-one-out crossvalidation method for different feature detection window size M ranging from
3 to 21.

Fig. 4 shows the recognition accuracy rates obtained for
Algorithm 1 Emotion Recognition using Min-Max classifier different normalization and feature detection window sizes
Require: Test image Y,Train images Xt ,trainlen,window size ranging from 3 to 21 for a single trial.
N and M
1: Crop the images to a dimension of m × n
2: for t = 1 to trainlen do
3:
C(i, j) = Xt (i,j)−µ(i,j)
q 6σ(i,j)
Pb
Pb
4:
W (i, j) = M12 k=−b h=−b [C(k + i, h + j) − µ(i, j)]2
5:
Store the value of W to an array train of dimension
trainlen × m × n
6: end for
7: for t = 1 to trainlen do
8:
V (i, j) = Y (i,j)−µ(i,j)
6σ(i,j)
q
Pb
Pb
9:
test(i, j) = M12 k=−b h=−b [V (k + i, h + j) − µ(i, j)]2
min[train(t,j),test(1,j)] 3
s(t, j) = [ max[train(t,j),test(1,j)]
]
Pm×n
11:
z(t) = j=1 s(t, j)
12:
out = max(z(t))
13: end for

10:

Fig. 4. Graph shows the accuracy rates obtained for different normalization
and feature detection window sizes ranging from M=N=3 to 21 for one trial
of leave-one-out cross-validation method.

To evaluate the performance of the proposed Min-Max
classifier, it has been compared with the other classifiers,
such as Nearest Neighbor [38] and Random Forest [39], after
normalization and feature detection. The obtained accuracy
values are shown in Table I.
TABLE I
T ESTING OF FEATURE DETECTED IMAGES ON OTHER CLASSIFIERS
Classif ier
Nearest Neighbor
Random Forest
Proposed Min-Max classifier

Accuracy(%)
92.85
88.57
98.57

The proposed method achieves a maximum accuracy of
98.57% for a window size of M = N = 11 which outperforms
the other existing methods in the literature for leave-one-out
cross-validation method. Table II shows the performance comparison of the proposed method with the other existing systems
on the same database using leave-one-out cross-validation
method. Applying the proposed method, we achieved emotion
recognition accuracy of 98.57% proving the significance of
emotion detection method with Min-Max similarity classifier.
TABLE II
C OMPARISON OF PROPOSED EMOTION RECOGNITION SYSTEM WITH
OTHER EXISTING SYSTEM BASED ON LEAVE - ONE - OUT CROSS - VALIDATION

methods. The effect of the proposed Min-Max classification
method on recognition accuracy is also important. Table I
shows the application of the other classification method for the
same approach where proposed Min-Max classifier illustrates
the performance improvement. In comparison to the existing
method, the proposed Min-Max classifier has an ability to
suppress the outliers that significantly impacts overall performance of this approach.
The simplicity and straightforwardness of the proposed
approach are also important due to the resultant low computational complexity. Most of the existing methods use complicated feature extraction and classification approaches that
double the complexity of the facial recognition process and
require the device with large computational capacity to process
the images. We address this problem applying direct local
mean and standard deviation based feature detection methods
and simple Min-Max classification method. In comparison to
the existing feature detection methods, such as PCA [27] and
LDA [8], the proposed method is straightforward and does
not require dimensionality reduction. Moreover, simple MinMax classification method also reduce the computational time,
comparing to the traditional classification approaches, such
as SVM [28], KNN [33], Gaussian process [24] and Neural
Network [41]. Therefore, the algorithm can be run on the
device with low computational capacity.

METHOD

Existing systems
Cheng et. al [24]
Hamester et. al [40]
Frank et. al [8]
Poursaberi et. al [23]
Hegde et. al [29]
Proposed method

M ethod used
Gaussian Process
Convolutional Neural
Network
DWT + 2D-LDA
+SVM
Gauss
Laguerre
wavelet+ KNN
Gabor and geometry
based features
Min-Max classifier

Accuracy(%)
93.43
95.80
95.71
96.71
97.14
98.57

In addition, comparing to the proposed emotion recognition
system, the other existing methods require specialized feature
extraction and dimensionality reduction techniques before
classification stage. The main advantages of the proposed
emotion recognition system are its simplicity and straightforwardness.
V. D ISCUSSION
The main advantages of the proposed facial motion recognition approach are high recognition accuracy and low computational complexity. To achieve high recognition accuracy,
the effective feature selection is required. In the existing
methods, the complex algorithms for feature selection are
applied without normalizing the image. The normalization
stage is important and has a considerable effect on the accuracy
of the feature selection process. In the proposed algorithm,
we apply simple pre-processing methods to normalize the
images and eliminate intensity offsets that effects the accuracy
of the feature selection process and leads to the increase of
emotion recognition accuracy, in comparison to the existing

VI. C ONCLUSION
In this paper, we have represented the approach to improve the performance of emotion recognition task using
template matching method. We have demonstrated that the
pixel normalization and feature extraction based on local
mean and standard deviation followed up by the Mix-Max
similarity classification can result in the improvement of
overall classification rates. We achieved emotion recognition
accuracy of 98.57% that exceeds the performance of the
existing methods for the JAFFE database for leave-one-out
cross-validation method. The capability of the algorithm to
suppress feature outliers and remove intensity offsets results
in the increase of emotion recognition accuracy. Moreover,
the proposed method is simple and direct, in comparison to
the other existing methods requiring the application of dimensionality reduction techniques and complex classification
methods for computation and analysis. Low computational
complexity is a noticeable benefit of the proposed algorithm
that implies the reduction of computational time and required
memory space. This method can be extended to the other
template matching problems, such as face recognition and
biometric matching. The drawback of the proposed method, as
in any other template matching method, is the metric learning
requiring to create the templates for each class that, in turn,
consumes additional memory space to store the templates.
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