Authors :
Saikat Goswami; Tanvir Ahmed Siddiqee; Khurshedul Barid; Shuvendu Mozumder Pranta
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/55nepzbh
Scribd :
https://tinyurl.com/3m7zuytn
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR1662
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Facial expressions have long been a
straightforward way for humans to determine emotions,
but computer systems find it significantly more difficult
to do the same. Emotion recognition from facial
expressions, a subfield of social signal processing, is
employed in many different circumstances, but is
especially useful for human-computer interaction.
Many studies have been conducted on automatic
emotion recognition, with the majority utilizing
machine learning techniques. However, the
identification of basic emotions such as fear, sadness,
surprise, anger, happiness, and contempt remains a
challenging subject in computer vision. Recently, deep
learning has gained more attention as potential
solutions for a range of real-world problems, such as
emotion recognition. In this work, we refined the
convolutional neural network method to discern seven
basic emotions and assessed several preprocessing
approaches to illustrate their impact on CNN
performance. The goal of this research is to enhance
facial emotions and features by using emotional
recognition. Computers may be able to forecast mental
states more accurately and respond with more
customised answers if they can identify or recognise the
facial expressions that elicit human responses.
Consequently, we investigate how a convolutional
neural network-based deep learning technique may
enhance the recognition of emotions from facial features
(CNN). Consequently, we investigate how a
convolutional neural network-based deep learning
technique may enhance the recognition of emotions
from facial features (CNN). Our dataset, which
comprises of roughly 32,298 pictures for testing and
training, includes multiple face expressions. After noise
removal from the input image, the pretraining phase
helps reveal face detection, including feature extraction.
The preprocessing system helps with this.
Keywords :
Facial Expression, Recognition, Classifications, Deep Learning Algorithm.
Facial expressions have long been a
straightforward way for humans to determine emotions,
but computer systems find it significantly more difficult
to do the same. Emotion recognition from facial
expressions, a subfield of social signal processing, is
employed in many different circumstances, but is
especially useful for human-computer interaction.
Many studies have been conducted on automatic
emotion recognition, with the majority utilizing
machine learning techniques. However, the
identification of basic emotions such as fear, sadness,
surprise, anger, happiness, and contempt remains a
challenging subject in computer vision. Recently, deep
learning has gained more attention as potential
solutions for a range of real-world problems, such as
emotion recognition. In this work, we refined the
convolutional neural network method to discern seven
basic emotions and assessed several preprocessing
approaches to illustrate their impact on CNN
performance. The goal of this research is to enhance
facial emotions and features by using emotional
recognition. Computers may be able to forecast mental
states more accurately and respond with more
customised answers if they can identify or recognise the
facial expressions that elicit human responses.
Consequently, we investigate how a convolutional
neural network-based deep learning technique may
enhance the recognition of emotions from facial features
(CNN). Consequently, we investigate how a
convolutional neural network-based deep learning
technique may enhance the recognition of emotions
from facial features (CNN). Our dataset, which
comprises of roughly 32,298 pictures for testing and
training, includes multiple face expressions. After noise
removal from the input image, the pretraining phase
helps reveal face detection, including feature extraction.
The preprocessing system helps with this.
Keywords :
Facial Expression, Recognition, Classifications, Deep Learning Algorithm.
