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  4. Yuno: Augmenting Human Memory in Social Contexts Through Real-Time Face Recognition and Contextual Recall
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Yuno: Augmenting Human Memory in Social Contexts Through Real-Time Face Recognition and Contextual Recall

Journal
Companion Publication of the 2026 ACM Designing Interactive Systems Conference
Type
Other
Date Issued
2026-06-12
Author(s)
Sutter, Robin
;
Stieger, Benjamin
;
Holenstein, Yannik
;
Yu Sun  
;
Florian Mathis  
DOI
10.1145/3802974.3808029
Abstract
Remembering faces and the contexts in which people were previously encountered is cognitively demanding. In this interactivity paper, we present Yuno, an AI-powered system that augments human social memory by combining real-time face recognition with contextual recall of relationship information. Yuno integrates smart glasses that provide in-situ conversational assistance with a gamified companion that supports the practices of active recall. The smart glasses capture facial features and conversational cues during initial encounters and deliver personalized voice-based reminders during subsequent encounters. The companion 64$\times$64 RGB LED matrix display gamifies social memory practices through AI-generated interactive quizzes, fostering engagement and relationship maintenance. Our work contributes to the HCI community by exploring the design and development of AI systems to support reconnecting with people in socially dense environments through social memory assistance, while also pointing toward accessibility-oriented applications for individuals with face blindness (prosopagnosia) or other memory difficulties.
Language
English (United States)
Keywords
Face recognition
wearable computing
augmenting memory
human augmentation
AI assistant
ubiquitous computing
HSG Classification
contribution to practical use / society
Refereed
No
Publisher
ACM
Official URL
https://dl.acm.org/doi/pdf/10.1145/3802974.3808029
URL
https://alexandria.unisg.ch/handle/20.500.14171/132663
Subject(s)

computer science

Division(s)

SCS - School of Compu...

File(s)
Thumbnail Image

open.access

Name

Manuscript.pdf

Size

2.51 MB

Format

Adobe PDF

Checksum (MD5)

e989b1e66ce39606f0fe223ca58a9b85

Support
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