FLEPNet: Feature Level Ensemble Parallel Network for Facial Expression Recognition

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50019494" target="_blank" >RIV/62690094:18450/22:50019494 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/9896934" target="_blank" >https://ieeexplore.ieee.org/document/9896934</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TAFFC.2022.3208309" target="_blank" >10.1109/TAFFC.2022.3208309</a>

Result language

angličtina

Original language name

FLEPNet: Feature Level Ensemble Parallel Network for Facial Expression Recognition

Original language description

With the advent of deep learning, the research on facial expression recognition (FER) has received a lot of interest. Different deep convolutional neural network (DCNN) architectures have been developed for real-time and efficient FER. One of the challenges in FER is obtaining trustworthy features that are strongly associated with facial expression changes. Furthermore, traditional DCNNs for FER problems have two significant issues: insufficient training data, which leads to overfitting, and intra-class facial appearance variations. FLEPNet, a texture-based feature-level ensemble parallel network for FER, is proposed in this study and proved to solve the aforementioned problems. Our parallel network FLEPNet uses multi-scale convolutional and multi-scale residual block-based DCNN as building blocks. First, we consider modified homomorphic filtering to normalize the illumination effectively, which minimizes the intra-class difference. The deep networks are then protected against having insufficient training data by using texture analysis on face expression images to identify multiple attributes. Four texture features are extracted and combined with the image&apos;s original characteristics. Finally, the integrated features retrieved by two networks are used to classify seven facial expressions. Experimental results reveal that the proposed technique achieves an average accuracy of 0.9914, 0.9894, 0.9796, 0.8756, and 0.8072 on Japanese Female Facial Expressions, Extended CohnKanade, Karolinska Directed Emotional Faces, Real-world Affective Face Database, and Facial Expression Recognition 2013 databases, respectively. Moreover, experimental outcomes depict significant reliability when compared to competing approaches. IEEE

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Transactions on Affective Computing

ISSN

1949-3045

e-ISSN

1949-3045

Volume of the periodical

13

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

2058-2070

UT code for WoS article

000892948500028

EID of the result in the Scopus database

2-s2.0-85139441989