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Related Experiment Video

Updated: Feb 25, 2026

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
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FaceScanPaliGemma multi-agent vision language models for facial attribute recognition.

Nouar AlDahoul1, Myles Joshua Toledo Tan2, Harishwar Reddy Kasireddy2

  • 1Computer Science Department, New York University Abu Dhabi, Abu Dhabi, UAE.

Scientific Reports
|February 23, 2026
PubMed
Summary
This summary is machine-generated.

FaceScanPaliGemma, a novel multi-agent vision language model (VLM), achieves high accuracy in classifying facial attributes like race, gender, age, and emotion. This system outperforms existing models in zero-shot evaluations.

Keywords:
FaceScanPaliGemmaFacial attribute recognitionMulti-agentVision language models

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial attribute recognition technologies have diverse applications but face challenges due to complexity and representation diversity.
  • Existing methods for facial attribute classification show a need for improved accuracy.

Purpose of the Study:

  • To propose FaceScanPaliGemma, a multi-agent vision language model (VLM) system for enhanced facial attribute classification.
  • To evaluate the performance of the proposed system against other state-of-the-art VLMs.

Main Methods:

  • Developed FaceScanPaliGemma, a system comprising four fine-tuned Google PaliGemma models, each specialized for a distinct facial attribute.
  • Utilized public datasets, FairFace and AffectNet, for comprehensive evaluation of the system's classification capabilities.

Main Results:

  • Achieved high accuracy rates: 81.1% for race, 95.8% for gender, 80.0% for age group, and 59.4% for emotion.
  • Demonstrated superior performance compared to OpenAI GPT, Google Gemini, LLaVA, and Google PaliGemma in zero-shot evaluations.

Conclusions:

  • The proposed FaceScanPaliGemma system offers a significant advancement in facial attribute classification accuracy.
  • The multi-agent approach with specialized models shows promise for complex vision language tasks.