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Paper

Towards Personalised Gaming via Facial Expression Recognition

by Independent / Community 00d06bcade3ce15636a0b0ed6d8209164a5f9a69

Free2AITools Nexus Index

69.8

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 86

P: Popularity 62

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

In this paper we propose an approach for personalising the space in which a game is played (i.e., levels) dependent on classifications of the user's facial expression — to the end of tailoring the affective game experience to the individual user. Our approach is aimed at online game personalisation, i.e., the game experience is personalised during actual play of the game. A key insight of this paper is that game personalisation techniques can leverage novel computer vision-based techniqu...

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Semantic Scholar 83 Citations

Paper Information Summary
Entity Passport
Registry ID	00d06bcade3ce15636a0b0ed6d8209164a5f9a69
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{00d06bcade3ce15636a0b0ed6d8209164a5f9a69,
  author = {Unknown},
  title = {Towards Personalised Gaming via Facial Expression Recognition Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/00d06bcade3ce15636a0b0ed6d8209164a5f9a69}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Towards Personalised Gaming via Facial Expression Recognition [Paper]. Free2AITools. https://api.semanticscholar.org/00d06bcade3ce15636a0b0ed6d8209164a5f9a69

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 86

Popularity (P) 62

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Towards Personalised Gaming via Facial Expression Recognition: Authority (A:86), Popularity (P:62), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

" In this paper we propose an approach for personalising the space in which a game is played (i.e., levels) dependent on classifications of the user's facial expression — to the end of tailoring the affective game experience to the individual user. Our approach is aimed at online game personalisation, i.e., the game experience is personalised during actual play of the game. A key insight of this paper is that game personalisation techniques can leverage novel computer vision-based techniqu..."

❝ Cite Node

@article{Unknown2026Towards,
  title={Towards Personalised Gaming via Facial Expression Recognition},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📈83CitationsSemantic Scholar

🏛️86AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️text generationField

🏷️ Research Topics

vision modelsrag retrieval

📦Data Source: semantic_scholar

🔄 Updated daily

Source summary: Based on semantic_scholar metadata. Not a recommendation.

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🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 0
stars: null
forks: null
citations: 83

Data indexed from public sources. Updated daily.