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Muscles for Facial Expressions01:14

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The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
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Related Experiment Video

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Image Captioning with Object Detection and Facial Expression Recognition for Smart Industry.

Abdul Saboor Khan1, Abdul Haseeb Khan2, Muhammad Jamshed Abbass3

  • 1Department of Electrical Engineering and Information Technology, Otto-von-Guericke University, 39106 Magdeburg, Germany.

Bioengineering (Basel, Switzerland)
|December 30, 2025
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Summary
This summary is machine-generated.

This study introduces an AI image captioning system integrating facial expression recognition for enhanced emotional context. The novel approach improves caption accuracy and emotional understanding, outperforming existing methods.

Keywords:
Convolutional Neural Networks (CNN)Edge AIHSEIndustry 4.0IoTVision-Language Pre-Training (VLP)deep learningfacial expression recognitionhuman–robot collaborationimage captioningmultimodal deep learningobject detectionpredictive maintenance

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Affective Computing

Background:

  • Image captioning models often lack emotional and contextual depth.
  • Integrating affective cues can improve the semantic richness of generated captions.

Purpose of the Study:

  • To develop an image captioning system incorporating facial expression recognition.
  • To enhance emotional and contextual comprehension in AI-generated captions.
  • To evaluate the system's performance against established benchmarks.

Main Methods:

  • A novel system combining affective cues and visual features for image captioning.
  • Experiments conducted on custom datasets (FlickrFace11k, COCOFace15k).
  • Evaluation using standard metrics: BLEU, METEOR, ROUGE-L, CIDEr, and SPICE.

Main Results:

  • The proposed model significantly outperformed baseline models (Show-Attend-Tell, Up-Down) across all metrics.
  • Achieved notable gains of 2.5 points on CIDEr and 1.0 on SPICE.
  • Demonstrated robustness via 5-fold cross-validation with minimal standard deviation (<±0.2).

Conclusions:

  • The system effectively captures fine-grained emotional expressions, surpassing conventional models.
  • Potential applications include affective computing, assistive technologies, and human-centric AI.
  • The pipeline supports on-prem/edge deployment and Industry 4.0 integration.