Transfer Learning: An Enhanced Feature Extraction Techniques for Facial Emotion Classification
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Date
2021-07
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
The process of extracting features from images
from the scratch in the field of machine learning is a very
challenging and time-consuming task. Previous work focused on
a technique for feature extraction in which a CNN is pre-trained
on some data sets and then in turn used to learn the pattern
from other datasets on which it was not originally trained. This
paper proposed transfer learning as the ability of a pre-trained
CNN model to learn patterns from data which it was not
originally trained on. Several pre-trained CNN architectures
have been proposed by researchers to improve the system
performance. Some of these include the ResNet, Xception,
VGG16, VGG19, InceptionResnetV2, InceptionV3, DenseNet,
MobileNet, etc., all trained on ImageNet datasets. However, not
all of these networks have the ability to effectively perform
feature extraction on facial datasets. This paper attempts to
compare the efficiencies of five pre-trained networks namely,
VGG19, VGG16, ResNet50, inceptionResNetV3, and
InceptionV2 which have been pre-trained on the ImageNet
dataset as features extractors on the DISFA plus facial emotion
dataset for classification. The extracted features are used for
training and testing a simple linear regression classifier. Testing
the classifier network with features extracted by these networks
produced different accuracies. It was discovered in the process
that out of the five networks being tested, the VGG19
architecture performed more accurately than other pre-trained
networks on the classifier with an accuracy of 97%. The work,
therefore, presumes that the VGG19 network has a better
generalization ability on facial data than other networks.
Description
Keywords
Convolutional Neural Network, Machine Learning, Transferred Learning, Pre-Trained Network
Citation
(1) Christine Bukola Asaju, Ugwu Kingsley, Eze Chinemerem Christian, Ugbedeojo Musa (2021) Transfer Learning: An Enhanced Feature Extraction Technique for Facial Emotion Classification. International Journal of Mechatronics, Electrical and Computer Technology (IJMEC) ISSN: 2305-0543 (Online), ISSN: 2411-6173 (Print) Vol. 11(41), Jul. 2021, PP. 5043-5045