View in EDS

Development of Facial Recognition-Based Toddler's Emotion Prediction System

Saved in:
Bibliographic Details
Title: Development of Facial Recognition-Based Toddler's Emotion Prediction System
Language: English
Authors: G. K. Afolabi-Yusuf, B. A. Bashiru, F. A. Odutayo
Source: Southeast Asia Early Childhood. 2025 14(2):103-110.
Availability: National Child Development Research Centre. Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia. e-mail: ncdrc@upsi.edu.my; Web site: https://ejournal.upsi.edu.my/index.php/SAECJ/Home
Peer Reviewed: Y
Page Count: 8
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Toddlers, Recognition (Psychology), Human Body, Emotional Response, Visual Stimuli, Algorithms, Psychological Patterns, Predictor Variables, Accuracy, Artificial Intelligence, Child Behavior
ISSN: 2821-3149
Abstract: Young children express their feelings through facial or verbal expressions that differ from person to person and are shaped by the environments where they live. Neglecting to understand and estimate how toddler emotions change can lead to delayed intervention timing, resulting in harm to their mental and social development processes. The study initiated the development of a system based on facial recognition processes to forecast toddler emotional responses. The random forest algorithm was used to build the system model, which received training from a dataset comprising 2,168 pictures showing both facial expressions of happiness and sadness. Mediapipe, a machine learning algorithm, was used for feature extraction. The model was then integrated into a user-friendly interface designed for ease of use. This interface captures a toddler's facial image and classifies their emotion as either happy or sad. In conclusion, the developed model demonstrated strong performance, achieving an accuracy of 84%. By providing real-time emotion predictions, the system can assist parents and caregivers in responding appropriately to a toddler's emotional state.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1498224
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=EJ1498224
    Name: ERIC Full Text
    Category: fullText
    Text: Full Text from ERIC
Header DbId: eric
DbLabel: ERIC
An: EJ1498224
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Development of Facial Recognition-Based Toddler's Emotion Prediction System
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22G%2E+K%2E+Afolabi-Yusuf%22">G. K. Afolabi-Yusuf</searchLink><br /><searchLink fieldCode="AR" term="%22B%2E+A%2E+Bashiru%22">B. A. Bashiru</searchLink><br /><searchLink fieldCode="AR" term="%22F%2E+A%2E+Odutayo%22">F. A. Odutayo</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Southeast+Asia+Early+Childhood%22"><i>Southeast Asia Early Childhood</i></searchLink>. 2025 14(2):103-110.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: National Child Development Research Centre. Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia. e-mail: ncdrc@upsi.edu.my; Web site: https://ejournal.upsi.edu.my/index.php/SAECJ/Home
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 8
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Toddlers%22">Toddlers</searchLink><br /><searchLink fieldCode="DE" term="%22Recognition+%28Psychology%29%22">Recognition (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Human+Body%22">Human Body</searchLink><br /><searchLink fieldCode="DE" term="%22Emotional+Response%22">Emotional Response</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+Stimuli%22">Visual Stimuli</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+Patterns%22">Psychological Patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Child+Behavior%22">Child Behavior</searchLink>
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2821-3149
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Young children express their feelings through facial or verbal expressions that differ from person to person and are shaped by the environments where they live. Neglecting to understand and estimate how toddler emotions change can lead to delayed intervention timing, resulting in harm to their mental and social development processes. The study initiated the development of a system based on facial recognition processes to forecast toddler emotional responses. The random forest algorithm was used to build the system model, which received training from a dataset comprising 2,168 pictures showing both facial expressions of happiness and sadness. Mediapipe, a machine learning algorithm, was used for feature extraction. The model was then integrated into a user-friendly interface designed for ease of use. This interface captures a toddler's facial image and classifies their emotion as either happy or sad. In conclusion, the developed model demonstrated strong performance, achieving an accuracy of 84%. By providing real-time emotion predictions, the system can assist parents and caregivers in responding appropriately to a toddler's emotional state.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2026
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1498224
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1498224
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 103
    Subjects:
      – SubjectFull: Toddlers
        Type: general
      – SubjectFull: Recognition (Psychology)
        Type: general
      – SubjectFull: Human Body
        Type: general
      – SubjectFull: Emotional Response
        Type: general
      – SubjectFull: Visual Stimuli
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Psychological Patterns
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Accuracy
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Child Behavior
        Type: general
    Titles:
      – TitleFull: Development of Facial Recognition-Based Toddler's Emotion Prediction System
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: G. K. Afolabi-Yusuf
      – PersonEntity:
          Name:
            NameFull: B. A. Bashiru
      – PersonEntity:
          Name:
            NameFull: F. A. Odutayo
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-electronic
              Value: 2821-3149
          Numbering:
            – Type: volume
              Value: 14
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Southeast Asia Early Childhood
              Type: main
ResultId 1