Facial Emotion Recognition using Artificial Intelligence Techniques

Features are the most basic yet impactful to capture emotions. Emotions are the source to depict the person's state of mind. This acts as the mind reader of a person. Emotions are not long-lasting and remain only for a moment. They vary vibrantly and currently, the mental health of a person has...

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Bibliographic Details
Main Authors: Rahul, Nidumukkula V N D S, NagaIndira S Geethika, Sukamanchi, Gayatri, Vemulakonda, Pranathi, K
Format: Conference Proceeding
Language:English
Subjects:
Artificial Intelligence
Convolutional Neural Network
Emotion detection
Emotion recognition
Face Detection
Face recognition
Feature extraction
Libraries
Live picture analysis
Mental health
Neural networks
Open CV
Support Vector Machine
Support vector machines
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Summary:Features are the most basic yet impactful to capture emotions. Emotions are the source to depict the person's state of mind. This acts as the mind reader of a person. Emotions are not long-lasting and remain only for a moment. They vary vibrantly and currently, the mental health of a person has a prominent role to play in the factor of efficiency of that person's outcome. Emotions have to be recognized and analyzed to determine the person's opinion or to understand the person's condition. Emotion detection has a prominent role to be played in different sectors such as psychology, research, corporate field, cognitive science, and many more. Indeed, micro-expressions depict the actual state of the person. This research work has proposed a sophisticated approach to obtain the person's state of mind by extracting the features. Initially, the faces are detected and using Artificial Intelligence techniques such as Convolution Neural Network (CNN) and Support Vector Machine (SVM) algorithms are implemented to classify the emotion on the FER-2013 dataset. Open CV library is imported to preprocess the image and to extract the image detected. Live picture analysis assists us to stand unique and detect the emotion in the most prescribed manner. Along with living capture motion, video analysis is also combined to capture the micro-expressions that portray the individual's most precise feelings. This model recognizes all the faces present in a picture or a video and can detect them individually.
ISSN:2768-5330
DOI:10.1109/ICICCS53718.2022.9788166