Emotion recognition using fourier transform and genetic programming
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gp-bibliography.bib Revision:1.7964
- @Article{ACHARYA:2020:AA,
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author = "Divya Acharya and Anosh Billimoria and
Neishka Srivastava and Shivani Goel and Arpit Bhardwaj",
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title = "Emotion recognition using fourier transform and
genetic programming",
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journal = "Applied Acoustics",
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year = "2020",
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volume = "164",
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pages = "107260",
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month = jul,
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keywords = "genetic algorithms, genetic programming,
Electroencephalogram, Fast Fourier Transform, Emotion
recognition, Movie clips, Cinema Films",
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ISSN = "0003-682X",
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URL = "http://lrcdrs.bennett.edu.in:80/handle/123456789/1183",
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URL = "http://www.sciencedirect.com/science/article/pii/S0003682X19306954",
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DOI = "doi:10.1016/j.apacoust.2020.107260",
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abstract = "In cognitive science, the real-time recognition of
humans emotional state is pertinent for machine
emotional intelligence and human-machine interaction.
Conventional emotion recognition systems use subjective
feedback questionnaires, analysis of facial features
from videos, and online sentiment analysis. This
research proposes a system for real-time detection of
emotions in response to emotional movie clips. These
movie clips elicitate emotions in humans, and during
that time, we have recorded their brain signals using
Electroencephalogram (EEG) device and analyze their
emotional state. This research work considered four
class of emotions (happy, calm, fear, and sadness).
This method leverages Fast Fourier Transform (FFT) for
feature extraction and Genetic Programming (GP) for
classification of EEG data. Experiments were conducted
on EEG data acquired with a single dry electrode device
NeuroSky Mind Wave 2. To collect data, a standardized
database of 23 emotional Hindi film clips were used.
All clips individually induce different emotions, and
data collection was done based on these emotions
elicited as the clips contain emotionally inductive
scenes. Twenty participants took part in this study and
volunteered for data collection. This system classifies
four discrete emotions which are: happy, calm, fear,
and sadness with an average of 89.14percent accuracy.
These results demonstrated improvements in
state-of-the-art methods and affirmed the potential use
of our method for recognising these emotions",
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notes = "Also known as \cite{ACHARYA2020107260}",
- }
Genetic Programming entries for
Divya Acharya
Anosh Faredoon Billimoria
Neishka Srivastava
Shivani Goel
Arpit Bhardwaj
Citations