FUZZY PREPROCESSING OF VIOLA-JONES ALGORITHM FOR FACE RECOGNITION IN THERMAL IMAGES

International Journal of Computer Science, Engineering and Information Technology (IJCSEIT),
Vol.13, No.5, October 2023
Dhinaharan Nagamalai (Eds): CGASP, VCOI, BLKCA -2023
pp. 21-28, 2023. IJCI – 2023 DOI : 10.5121/ijcseit.2023.13502
FUZZY PREPROCESSING OF VIOLA-JONES
ALGORITHM FOR FACE RECOGNITION IN
THERMAL IMAGES
Edwyn Martínez, Edmundo Bonilla, Eduardo Sánchez, Crispín Hernández and
Roberto Morales
Tecnológico Nacional de México, Apizaco, México
ABSTRACT
This paper proposes to improve the Viola-Jones algorithm to identify facial features in thermal images
using a fuzzy linguistic modifier. The purpose is to improve the image quality in the infrared plane and to
facilitate the recognition stage, feature selection and emotion classification. The results obtained show that
adding a preprocessing stage improves the capability of the Viola-Jones algorithm in identifying facial
features in thermal images.
KEYWORDS
Emotion recognition, Edge detection, Viola-Jones, Image processing
1. INTRODUCTION
Emotion recognition is an activity that has been studied by different disciplines: psychology,
sociology, medicine, philosophy, and human-computer interaction.
Different research has presented different methodologies to understand this activity, so they have
implemented devices from different areas, some cases using electrocardiograms[1], CW Radars
(Continuous Wave), RGB Cameras[2] and thermal cameras[3] to mention a few. Recently, the
study of emotional states has been widely addressed by artificial intelligence, which is known as
affective computing. This new methodology deals with the design of new electromechanical
systems and devices that can recognize, interpret, and process emotions[4]–[8]
2. RELATED WORKS
Emotion recognition can be applied to different areas of technology, and therefore further
development and/or advances can be obtained. One of the known processes consists of selecting
the facial regions from which the necessary characteristics are extracted for better emotion
recognition, in the article[9]they propose to make a personalized mark for each, they mention that
it is possible to obtain "a fingerprint with the characteristics that can be collected in a facial
thermal image".
Within their research they present that a thermal image can present problems due to the
illumination that is presented in the background of the face, so they considered the light in the
place where the tests were conducted, with this, they managed to make 3D models with which
you can collect data from a thermal image.

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The methods used for the recognition of emotions are mostly performed by means of RGB
cameras in addition to considering the positions of those involved and the lighting within the
sampling site, as this can define whether a sample is relevant or not. By means of this type of
cameras[10]collect 2D and 3D plane information about the face of the sample group at a distance
of 0.4 to 3.5 meters away from the RGB camera, as well as adjusting the tilt angle, placing 10
people with different characteristics and poses, they selected a tilt of 45 with respect to the head
of each person.
The collection of information for emotion recognition is mostly focused on those characteristics
that can be found in the facial area, so tools such as RGB and thermal cameras are used to obtain
such information.
Although a good emotion recognition can be obtained by monitoring a person's face, there is
research that proposes to monitor different areas of the body.By means of two cameras connected
to a PC respectively,[11] captured the facial area and the rest of the body, in order to record the
changes that occurred. With data from both cameras, they achieved a recognition rate of 91.1%
by adding the values obtained.
In different investigations they have used different devices to extract features from one or
different areas such as the face and the body, although they have presented extractions with two
RGB cameras in these directions,[2]presented a combination between a camera and a CW radar
(radar that transmits and receives continuous waves) in order to find a greater amount of
movements that allow a better extraction of features.
For the correct collection, the participants' faces must be correctly illuminated, and the
environment must be configured according to the same rule. This work obtained an accuracy with
these methods of 89.30%.
With the knowledge acquired, the necessary stimuli were considered to find the selected
emotional states, such mental states can be induced through the presentation of video
clips[12]where scenes are shown that provoke the emotions found in Ekman's classification
which are: fear, sadness, anger, joy, surprise and disgust. The selection of such material has had
its respective changes throughout the years in which this type of experiments have been carried
out, since the most recent situation in COVID-19 has set a new flag for these resources, due to
the fact that the selection of audiovisual media that were already used as a reference previously
may not have the same intensity when it comes to achieving the main objective.Likewise, the
different backgrounds that may exist within the selected group of people affect the way in which
the stimuli take effect[13]. In order to have a better selection of the material with which to induce
the desired emotions, in addition to the elements presented in the images, videos and/or audios,
elements such as resolution, brightness, field of vision and contrast should be taken into account,
all this to achieve a better stimulation[14].
3. METHODS AND MATERIALS
3.1. Methodology
To carry out the process of this experiment, different variables were evaluated to obtain the best
results. In a group of 25 engineering students at the Technological Institute of Apizaco composed
of 12 women and 13 men with an age range of 20 to 26 years were exposed to visual stimuli to
collect the necessary samples, for this the following points were prepared: the environment where

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the tests were collected, the material used to stimulate the different moods, as well as the
necessary preparations before and after.
In the first point, an environment was configured using a laptop to control the order of
presentation of the stimuli, a Samsung LED monitor and being complemented with headphones
equipped with noise cancellation, in order to have a greater stimulus in the participants. To
collect samples within the configured environment, two cameras were used, a thermal camera
(Fluke thermal Camera Ti32) and an RGB camera (Sony Ciber-Shot DSC-H300).The thermal
camera was placed at a distance of 1 meter from the back of the monitor to capture the changes in
the face of the participants, and the RGB camera was placed to one side of it, which in addition to
capturing the facial area, focused on the upper part of the body to capture the movements made
by some participants. The participants were placed 50 centimetres away from the monitor in
order to obtain a better view of the clips presented.
The sampling process was as follows:
- Take the participants to the place where they were previously prepared.
- Explain what sampling consists of.
- Give a short questionnaire to know the condition in which the participants were carrying,
as well as ask for their authorization (by placing name and signature on the
questionnaire) to use the image collected.
- Display the selected multimedia material.
- After showing the material, ask about the emotional state in which the participants were,
in order to know if there was a change of emotion.
- Give thanks for participating.
Sampling was carried out from 10:00 to 13:00 approximately, for two reasons: it was considered
that this is a time when students are in the best conditions to be exposed to emotional states such
as stress and boredom; likewise, in this range of hours, daylight does not suffer significant
changes, so, being close to a window, there were no changes in lighting in the samples collected.
3.2. Materials
The material to stimulate the different emotional states was taken from the LATEMO-E video
base, which has a total of 70 video clips of emotions such as; disgust, anger, fear, sadness, fun,
tenderness and neutral, these videos are fragments of movies from different countries dubbed into
Spanish. To start with this selection process, the emotions that were to be induced were
considered, in principle, those emotions and clips that had a higher percentage of the target
emotion were taken into account. The table presented in the article [12]shows that the clips with
the highest percentage of the target is fear with 100%, followed by neutral, anger, fun, sadness,
disgust and tenderness with 96%, 93%, 91%, 90%, 90%, 90% and 79% respectively. Although
emotions such as anger and disgust had a high percentage, the clips presented showed scenes that
could be considered "strong" or "very explicit" for a certain group of people, so these emotions
were discarded, the neutral emotion was not considered for this sampling since we wanted to find
the changes that can be found in different emotions, so 4 emotions were selected; fun, fear,
tenderness and sadness, taking those clips with the highest percentage in the target emotion and
thus presenting it to the participants. The selected material is presented in the following table.

24
Table 1. Clips selected by emotion, as well as the success rate displayed in the database
Emotion Clip Percentage of excitement
achieved
Fun Blended (LMF)
The hangover (QPAI)
The hangover III (QPAIII)
The proposal (LPR)
85%
60%
87%
78%
Fear Mirrors (ESI)
The conjuring 2 (CON2)
93%
100%
75%
93%
Tenderness He’s just not that into you (SQN)
Her 2
Les choristes (LCO)
Pride and prejudice (OYP)
The notebook (DUPI)
79%
53%
61%
65%
70%
Sadness My sister’s keeper (DMD)
Never let me go (NMA)
The boy in the striped pajamas (NPR)
The impossible (LIM)
89%
75%
90%
85%
4. PROPOSED MODEL
To improve the process of identifying facial features in thermal images, the following model is
proposed (Figure 2). In a first stage, the thermal image base is read; this process is described in
detail in section III.I. For each image in the infrared plane, the 3 RGB color channels are
converted into grayscale using the following formula:
𝐼𝑚𝑎𝑔𝑒𝐺𝑟𝑎𝑦 = 𝐶𝑎𝑛𝑎𝑙𝑅𝑒𝑑 ∗ 0.299 + 𝐶𝑎𝑛𝑎𝑙𝐺𝑟𝑒𝑒𝑛 ∗ 0.587 + 𝐶𝑎𝑛𝑎𝑙𝐵𝑙𝑢𝑒 ∗ 0.114 (1)
Fig 1. Proposed model described by steps.

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The next step is to apply the Viola-Jones algorithm to detect the contour of the person. Once the
region of interest is located, which in this case is the face, we proceed to convert the image into
interval [0,1] by dividing only the pixel values by 255, to apply the fuzzy linguistic
modifier𝐸𝑐𝐼𝑚𝑎𝑔𝑒:
𝐸𝑐𝐼𝑚𝑎𝑔𝑒 = min(𝐼𝑁𝑇(𝐼𝑚𝑎𝑔𝑒𝐺𝑟𝑎𝑦),1 − 𝐼𝑁𝑇(𝐼𝑚𝑎𝑔𝑒𝐺𝑟𝑎𝑦)
6
)) (2)
Where INT is the intensification operator, which is defined as follows [15]:
The operator 𝐸𝑐𝐼𝑚𝑎𝑔𝑒 is intended to perform a double intensification to increase the contrast level
in thermal images. The first one highlights the contrast in the image in gray levels, then a second
one raised to the power 6 is applied to increase the image resolution. We take from these two
intensified images the minimum membership value and obtain an equalized image that facilitates
the Viola-Jones algorithm to identify other regions of interest such as the mouth, nose and eyes.
In this paper we focus on the location of the nose and mouth regions. The nose to find a type of
pathology associated with a POST-COVID effect and the mouth to classify basic emotions
stimulated through audio-video, described in Section 3. 2.
The next stage is to extract facial features. For example, the Harris algorithm is applied to the
mouth to find the most representative points of the mouth contour and evaluate how open it is to
classify some type of emotion such as joy or fear.
5. RESULTS ANALYSIS
The following shows the results obtained with the proposal to apply a fuzzy linguistic modifier
named𝐸𝑐𝐼𝑚𝑎𝑔𝑒 the Viola-Jones algorithm is used in the preprocessing stage to improve the low
contrast when working with thermal images.
Figure 3 shows the results of applying only the Viola-Jones algorithm once the face is located.
The nose region is not located, and the mouth region is misidentified. As can be seen once the
thermal image is converted into gray levels, it is difficult to locate the regions that make up the
face such as the eyes, nose and mouth. The Viola-Jones algorithm fails 96% to locate a facial
region. If it does detect, the window of the region of interest is in a different area, such as the hair
region, cheeks, forehead or even eyebrows.

26
Fig 2. In this image Viola-Jones is unable to correctly detect mouth region.
Figure 3 shows the results of applying the proposed operator to improve contrast distribution in a
thermal image. With this operator, 76% of the main facial regions are located: nose, eyes, and
mouth. In other words, this percentage was obtained in 19 of the 25 images collected.
Fig 3. Result applying the fuzzy modifier as preprocesing and Viola-Jones algorithm for facial feature
detection.

27
Of the 19 images from which the nose and mouth regions were located. A binarization of each
region was applied to extract features. Figure 4 shows the binarized mouth region.
Fig 4. Binarization of region of interest: mouth.
Figure 5 shows the collection of features obtained with the Harris detector[16]. With this corner
detector, it is applied to know the mouth opening, for these the main 18 features are selected and
determine the height and proceed to classify the emotion. If the audiovisual stimulation was a
happy clip, then it is determined that there has been a change in the corners of the mouth and the
person is classified as reactive to the stimulus. If the mouth remains closed it indicates that the
person has not been stimulated by the video clip.
Fig 5. Main characteristics of the region of interest: mouth.
Based in these main characteristics, we extract points of height and width to calculate how much
the mouth is opening and to determine if the subject is smiling or not.
For this task we define the following fuzzy rules:
- IF height IS low THEN emotion IS slight happy.
- IF height IS medium THEN emotion is More or Less Happy.
- IF height IS high THEN emotion is Fairly Happy.
Fig 6. Fuzzy inference rules.

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According to the height obtained from Figure 5 and using a fuzzy inference system (shown in the
Figure 6) the subject, emotion is More or Less Happy.
6. CONCLUSIONS
Emotion recognition in the visible plane has been extensively studied for detecting facial features
with the Viola-Jones algorithm; however, it has been little used with images in the infrared plane
due to the limitations of the low contrast produced by thermal cameras. Thermal images can be
used to analyse thermal variations that can help a classifier to better categorize facial features. In
this paper we have proposed to solve the region identification problem with the Viola-Jones
algorithm using a linguistic operator. The results obtained are promising and, in the future, we
propose to use an emotion classifier algorithm such as a recommender system or a convolutional
method to improve this activity.
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