Facial Expression Recognition in-the-Wild Using Blended Feature Attention Network

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10255210" target="_blank" >RIV/61989100:27240/23:10255210 - isvavai.cz</a>

Alternative codes found

RIV/62690094:18450/23:50020670

Result on the web

<a href="https://ieeexplore.ieee.org/document/10250983" target="_blank" >https://ieeexplore.ieee.org/document/10250983</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TIM.2023.3314815" target="_blank" >10.1109/TIM.2023.3314815</a>

Result language

angličtina

Original language name

Facial Expression Recognition in-the-Wild Using Blended Feature Attention Network

Original language description

Facial expression (FE) analysis plays a crucial role in various fields, such as affective computing, marketing, and clinical evaluation. Despite numerous advances, research on FE recognition (FER) has recently been proceeding from confined laboratory circumstances to in-the-wild environments. FER is still an arduous and demanding problem due to occlusion and pose changes, intraclass and intensity variations caused by illumination, and insufficient training data. Most state-of-the-art (SOTA) approaches use the entire face for FER. However, past studies on psychology and physiology reveal that the mouth and eyes reflect the variations of various FEs, which are closely related to the manifestation of emotion. A novel method is proposed in this study to address some of the issues mentioned above. First, modified homomorphic filtering (MHF) is employed to normalize the illumination, then the normalized face image is cropped into five local regions to emphasize expression-specific characteristics. Finally, a unique blended feature attention network (BFAN) is designed for FER. BFAN consists of both residual dilated multiscale (RDMS) feature extraction modules and spatial and channel-wise attention (CWA) modules. These modules help to extract the most relevant and discriminative features from the high-level (HL) and low-level (LL) features. Then, both feature maps are integrated and passed on to the dense layers followed by a softmax layer to compute probability scores. Finally, the Choquet fuzzy integral is applied to the computed probability scores to get the final outcome. The superiority of the proposed method is exemplified by comparing it with 18 existing approaches on seven benchmark datasets.

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

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OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

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

1557-9662

Volume of the periodical

72

Issue of the periodical within the volume

5026416

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

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UT code for WoS article

001083291000016

EID of the result in the Scopus database

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