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Related Experiment Video

Updated: Apr 15, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Face averages enhance user recognition for smartphone security.

David J Robertson¹, Robin S S Kramer¹, A Mike Burton¹

¹School of Psychology, University of Aberdeen, Aberdeen, United Kingdom; Department of Psychology, University of York, York, United Kingdom.

|March 26, 2015

Summary

This summary is machine-generated.

Storing an average face image significantly improves smartphone face recognition accuracy. This method enhances security by making face verification more reliable across various conditions without increasing false rejections.

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Last Updated: Apr 15, 2026

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Area of Science:

Cognitive Psychology
Computer Vision
Biometrics

Background:

Human face recognition excels with familiar faces but struggles with unfamiliar ones.
Current automatic face recognition systems often perform poorly with unfamiliar faces.
Familiarity-based advantages could enhance automatic face recognition performance.

Purpose of the Study:

To investigate if incorporating 'face-average' representations can improve smartphone face verification.
To assess the effectiveness of face-average encoding in real-world viewing conditions.
To determine if this method impacts imposter face rejection rates.

Main Methods:

Two experiments were conducted using a popular smartphone's face verification system (Samsung Galaxy).
The system was encoded with an individual's 'face-average' representation, derived from human face perception theories.
Recognition performance was tested with unconstrained celebrity images and real user faces.

Main Results:

Face-average encoding significantly improved recognition performance for both celebrity and real faces.
Users could unlock their phones more reliably with an average face template than a single image.
Performance improvements were consistent across diverse everyday viewing conditions.
The method did not decrease the rejection rate of imposter faces.

Conclusions:

'Face-average' representation is a crucial factor for improving automatic face recognition systems.
This technique enhances face verification reliability in everyday settings.
Representation optimization is as vital as algorithm development for robust face recognition.