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Online Learning State Evaluation Method Based on Face Detection and Head Pose Estimation.

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  • Author(s): Li B;Li B; Liu P; Liu P
  • Source:
    Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Feb 20; Vol. 24 (5). Date of Electronic Publication: 2024 Feb 20.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE
    • Publication Information:
      Original Publication: Basel, Switzerland : MDPI, c2000-
    • Subject Terms:
      Education, Distance*; Humans ; Learning ; Students ; Algorithms ; Computers, Handheld
    • Abstract:
      In this paper, we propose a learning state evaluation method based on face detection and head pose estimation. This method is suitable for mobile devices with weak computing power, so it is necessary to control the parameter quantity of the face detection and head pose estimation network. Firstly, we propose a ghost and attention module (GA) base face detection network (GA-Face). GA-Face reduces the number of parameters and computation in the feature extraction network through the ghost module, and focuses the network on important features through a parameter-free attention mechanism. We also propose a lightweight dual-branch (DB) head pose estimation network: DB-Net. Finally, we propose a student learning state evaluation algorithm. This algorithm can evaluate the learning status of students based on the distance between their faces and the screen, as well as their head posture. We validate the effectiveness of the proposed GA-Face and DB-Net on several standard face detection datasets and standard head pose estimation datasets. Finally, we validate, through practical cases, that the proposed online learning state assessment method can effectively assess the level of student attention and concentration, and, due to its low computational complexity, will not interfere with the student's learning process.
    • References:
      IEEE Trans Pattern Anal Mach Intell. 2017 Jun;39(6):1137-1149. (PMID: 27295650)
      Sensors (Basel). 2021 May 14;21(10):. (PMID: 34069027)
      Sensors (Basel). 2022 Oct 21;22(20):. (PMID: 36298396)
    • Grant Information:
      20210103080 Jilin Science and Technology Bureau
    • Contributed Indexing:
      Keywords: face detection; head pose estimation; online learning state evaluation
    • Publication Date:
      Date Created: 20240313 Date Completed: 20240314 Latest Revision: 20240315
    • Publication Date:
      20240315
    • Accession Number:
      PMC10935162
    • Accession Number:
      10.3390/s24051365
    • Accession Number:
      38474900