DOI

10.5220/0014632500004084

Author ORCID Identifier

Arryn Robbins: https://orcid.org/0000-0002-8629-1210

Abstract

Estimating the distance between a user and their device’s camera is important for tasks such as gaze-aware interaction, adaptive interface scaling, and accessibility tools. Specialized depth sensors are effective but rarely available in everyday computing devices; by contrast, webcams are ubiquitous yet lack native distance information. To advance accessible distance-aware computing, we present Face2Cam, an openly available dataset of webcam images annotated with user-to-camera distances. Thirty-six adults were photographed under controlled variation of head orientation, gaze direction, and illumination, yielding 3,240 labeled images spanning distances from 30–80 cm. As a baseline, we trained a convolutional neural network as a lightweight reference to classify images into discrete distance intervals, achieving promising accuracy. These results demonstrate both the feasibility of webcam-only distance estimation and the value of Face2Cam as a resource for training and benchmarking distance-aware models, while leaving room for future architectural improvements.

Document Type

Article

Publication Date

2026

Publisher Statement

© 2026 by SCITEPRESS – Science and Technology Publications, Lda

Paper published under CC license (CC BY-NC-ND 4.0)

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Psychology Commons

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