Emotion recognition using fourier transform and genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{ACHARYA:2020:AA,

• author = "Divya Acharya and Anosh Billimoria and Neishka Srivastava and Shivani Goel and Arpit Bhardwaj",
• title = "Emotion recognition using fourier transform and genetic programming",
• journal = "Applied Acoustics",
• year = "2020",
• volume = "164",
• pages = "107260",
• month = jul,
• keywords = "genetic algorithms, genetic programming, Electroencephalogram, Fast Fourier Transform, Emotion recognition, Movie clips, Cinema Films",
• ISSN = "0003-682X",
• URL = "http://lrcdrs.bennett.edu.in:80/handle/123456789/1183",
• URL = "http://www.sciencedirect.com/science/article/pii/S0003682X19306954",
• DOI = "doi:10.1016/j.apacoust.2020.107260",
• 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",
• notes = "Also known as \cite{ACHARYA2020107260}",

}

Genetic Programming entries for Divya Acharya Anosh Faredoon Billimoria Neishka Srivastava Shivani Goel Arpit Bhardwaj

Citations