THE 3
rd
INTERNATIONAL SCIENTIFIC CONFERENCES OF STUDENTS AND YOUNG RESEARCHERS
dedicated to the 99
th
anniversary of the National Leader of Azerbaijan Heydar Aliyev
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computer systems today can work with digital images and videos to understand
and emotionally identify the characteristics of the video’s contents.
Cognitive Science and Sentiment Analysis
Artificial Intelligence today has reached heights and lengths that were
not imagined a few years back. Programs and computer systems can now
mimic human behavior, reactions, and responses to a high level of accuracy.
The basic task of any sentiment analysis program is to isolate the polarity
of the input (text, speech, facial expression, etc.) to understand whether the
primary sentiment presented is positive, negative, or neutral. Based on this
initial analysis, programs then often dig deeper to identify emotions like
enjoyment, happiness, disgust, anger, fear, and surprise.
Face Emotion Recognizer
The Face Emotion Recognizer (generally known as the FER) is an open-
source Python library and is used for sentiment analysis of images and
videos. The project is built on a version that uses a convolution neural
network with weights mentioned in the HDF5 data of this system’s creation
model. This can be overridden by using the FER constructor when the model
is called and initiated.
1. MTCNN (multi cascade convolutional network) is a parameter of the
constructor. It is a technique to detect faces. When it is set to ‘True’ the
MTCNN
model is used to detect faces, and when it is set to ‘False’ the
function uses the default OpenCV Haarcascade classifier.
2. detect_emotions(): This function is used to classify the detection of
emotion and it registers the output into six categories, namely, ‘fear’, ‘neutral’,
‘happy’, ’sad’, ‘anger’, and ‘disgust’. Every emotion is calculated, and the
output is put on a scale of 0 to 1.
Conclusion
The goal of this paper is to present comparative
study on different
techniques of video based facial and emotions recognition using different
methods. Since from last decade, automatic facial expression and emotions
recognition plays a significant role in our daily communication and computer
science, such as human-computer
interaction systems, biometrics, and
security and so on. In recent years, much deep and productive research in
this area has been carried out. The current methods research problems are
defined in this paper. For future work, new method designing should be done
in order to improve robustness, efficiency and accuracy of recognition.
References
[1] Landowska, M. Szwoch, W. Szwoch, M.R. Wróbel, A. Kołakowska, Emotion recognition
and
its applications
[2] Paweł Tarnowski, Marcin Kołodziej, Andrzej Majkowski, Remigiusz J. Rak,
Emotion
recognition using facial expressions, 2017
[3] Husam Salih, Lalit Kulkarni, Study of Video based Facial
Expression and Emotions
Recognition Mehotds, 2017