Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50017579" target="_blank" >RIV/62690094:18450/21:50017579 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/9226437" target="_blank" >https://ieeexplore.ieee.org/document/9226437</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TIM.2020.3031835" target="_blank" >10.1109/TIM.2020.3031835</a>

Result language

angličtina

Original language name

Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks

Original language description

An image is worth a thousand words; hence, a face image illustrates extensive details about the specification, gender, age, and emotional states of mind. Facial expressions play an important role in community-based interactions and are often used in the behavioral analysis of emotions. Recognition of automatic facial expressions from a facial image is a challenging task in the computer vision community and admits a large set of applications, such as driver safety, human-computer interactions, health care, behavioral science, video conferencing, cognitive science, and others. In this work, a deep-learning-based scheme is proposed for identifying the facial expression of a person. The proposed method consists of two parts. The former one finds out local features from face images using a local gravitational force descriptor, while, in the latter part, the descriptor is fed into a novel deep convolution neural network (DCNN) model. The proposed DCNN has two branches. The first branch explores geometric features, such as edges, curves, and lines, whereas holistic features are extracted by the second branch. Finally, the score-level fusion technique is adopted to compute the final classification score. The proposed method along with 25 state-of-the-art methods is implemented on five benchmark available databases, namely, Facial Expression Recognition 2013, Japanese Female Facial Expressions, Extended CohnKanade, Karolinska Directed Emotional Faces, and Real-world Affective Faces. The databases consist of seven basic emotions: neutral, happiness, anger, sadness, fear, disgust, and surprise. The proposed method is compared with existing approaches using four evaluation metrics, namely, accuracy, precision, recall, and f1-score. The obtained results demonstrate that the proposed method outperforms all state-of-the-art methods on all the databases. © 2020 IEEE.

Czech name

—

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

20201 - Electrical and electronic engineering

Project

<a href="/en/project/EF18_069%2F0010054" target="_blank" >EF18_069/0010054: IT4Neuro(degeneration)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Name of the periodical

IEEE Transactions on Instrumentation and Measurement

ISSN

0018-9456

e-ISSN

—

Volume of the periodical

70

Issue of the periodical within the volume

JANUARY

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

"Article Number: 5003512"

UT code for WoS article

000691803600005

EID of the result in the Scopus database

2-s2.0-85098583090