Efficientnet for human fer using transfer learning
By: Singh, Rajesh
.
Contributor(s): Sharma, Himanshu.
Publisher: Chennai ICT Academy 2022Edition: Vol.13(1), Oct.Description: 2792-2797p.Subject(s): Computer EngineeringOnline resources: Click here
In:
ICTACT Journal on Soft Computing (IJSC)Summary: Automatic facial expression recognition (FER) remained a
challenging problem in computer vision. Recognition of human facial
expression is difficult for machine learning techniques since there is a
variation in emotional expression from person to person. With the
advancement in deep learning and the easy availability of digital data,
this process has become more accessible. We proposed an efficient
facial expression recognition model based EfficientNet as backbone
architecture and trained the proposed model using the transfer
learning technique. In this work, we have trained the network on
publicly available emotion datasets (RAF-DB, FER-2013, CK+). We
also used two ways to compare our trained model: inner and cross-data
comparisons. In an internal comparison, the model achieved an
accuracy of 81.68 % on DFEW and 71.02 % on FER-2013. In a cross-
data comparison, the model trained on RAF-DB and tested on CK+
achieved 78.59%, while the model trained on RAF-DB and tested on
FER-2013 achieved 56.10% accuracy. Finally, we generated an t-SEN
distribution of our model on both datasets to demonstrate the model's
inter-class discriminatory power.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2023-0516 |
Automatic facial expression recognition (FER) remained a
challenging problem in computer vision. Recognition of human facial
expression is difficult for machine learning techniques since there is a
variation in emotional expression from person to person. With the
advancement in deep learning and the easy availability of digital data,
this process has become more accessible. We proposed an efficient
facial expression recognition model based EfficientNet as backbone
architecture and trained the proposed model using the transfer
learning technique. In this work, we have trained the network on
publicly available emotion datasets (RAF-DB, FER-2013, CK+). We
also used two ways to compare our trained model: inner and cross-data
comparisons. In an internal comparison, the model achieved an
accuracy of 81.68 % on DFEW and 71.02 % on FER-2013. In a cross-
data comparison, the model trained on RAF-DB and tested on CK+
achieved 78.59%, while the model trained on RAF-DB and tested on
FER-2013 achieved 56.10% accuracy. Finally, we generated an t-SEN
distribution of our model on both datasets to demonstrate the model's
inter-class discriminatory power.
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