Facial Expression Recognition and Face Recognition Using a Convolutional Neural Network

Dwijayanti, Suci; Abdillah, Rahmad; Hikmarika, Hera; Hermawati, -; Husin, Zaenal; Suprapto, Bhakti Yudho

doi:10.1109/isriti51436.2020.9315513

Search citation statements

Order By: Relevance

Paper Sections

Select...

Indonesian Sign Language1

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

2023

Publication Types

Select...

Article2

Other1

Relationship

Self Cite1

Independent2

Authors

Journals

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the process is performed using Softmax. Softmax calculates the probabilities for all target classes to determine the classes based on the input [22].…”

Section: Indonesian Sign Languagementioning

confidence: 99%

Indonesia Sign Language Recognition using Convolutional Neural Network

Dwijayanti¹,

Hermawati²,

Taqiyyah³

et al. 2021

IJACSA

Self Cite

View full text Add to dashboard Cite

“…Finally, the process is performed using Softmax. Softmax calculates the probabilities for all target classes to determine the classes based on the input [22].…”

Section: Indonesian Sign Languagementioning

confidence: 99%

Indonesia Sign Language Recognition using Convolutional Neural Network

Dwijayanti¹,

Hermawati²,

Taqiyyah³

et al. 2021

IJACSA

Self Cite

View full text Add to dashboard Cite

“…Meanwhile, Dwijayanti et al [12] used the dataset obtained via the capture of 52 facial patterns with four different facial expressions and different light intensities in outdoor conditions using a camera. Their results showed that the CNN can recognize both faces and facial expressions.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition-Based Room Access Security System Prototype using A Deep Learning Algorithm

Pohan,

Dwijayanti,

Suprapto

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

Writing Mandarin characters is considered the most challenging component for beginners due to the rules and character formations. This paper explores the potential of a machine learning-based digital learning tool to write Mandarin characters. It also conducts a comparative study between MobileNetV2 and MobileNetV3, exploring different configurations. The research follows the Multimedia Development Life Cycle (MDLC) method to create both application and machine learning models. Participants from higher education institutions that offer Mandarin courses in Batam, Indonesia, participated in a User Acceptance Test (UAT). Data were collected through questionnaires and analyzed using the System Usability Scale (SUS) methods. The results show positive user acceptance, with an SUS score of 77.92%, indicating a high level of acceptability. MobileNetV3Small was also preferred for recognizing user handwriting, due to comparable accuracy size, rapid inference time and smallest model size. Although the application was well received, several participants provided constructive feedback, suggesting potential improvements.

show abstract

Hands-Free Mouse Control Using Facial Feature

Agarwal,

Mustafa

et al. 2023

2023 International Conference on Smart Systems for Applications in Electrical Sciences (ICSSES)

View full text Add to dashboard Cite

Facial Expression Recognition and Face Recognition Using a Convolutional Neural Network

Cited by 3 publications

References 11 publications

Indonesia Sign Language Recognition using Convolutional Neural Network

Indonesia Sign Language Recognition using Convolutional Neural Network

Face Recognition-Based Room Access Security System Prototype using A Deep Learning Algorithm

Hands-Free Mouse Control Using Facial Feature

Contact Info

Product

Resources

About