# A Human-Centered Risk Evaluation of Biometric Systems Using Conjoint Analysis

arXiv: https://arxiv.org/abs/2409.11224 · HTML: https://mlsanity.com/paper/2409.11224/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [A Survey On Anti-Spoofing Methods For Face Recognition with RGB Cameras of Generic Consumer Devices](https://mlsanity.com/paper/2010.04145/) (2020)
- [Review of the Fingerprint Liveness Detection (LivDet) competition series: from 2009 to 2021](https://mlsanity.com/paper/2202.07259/) (2022)
- [Risk, Trust, and Bias: Causal Regulators of Biometric-Enabled Decision Support](https://mlsanity.com/paper/2008.02359/) (2020)

## Abstract (arXiv)

Biometric recognition systems, known for their convenience, are widely adopted across various fields. However, their security faces risks depending on the authentication algorithm and deployment environment. Current risk assessment methods faces significant challenges in incorporating the crucial factor of attacker's motivation, leading to incomplete evaluations. This paper presents a novel human-centered risk evaluation framework using conjoint analysis to quantify the impact of risk factors, such as surveillance cameras, on attacker's motivation. Our framework calculates risk values incorporating the False Acceptance Rate (FAR) and attack probability, allowing comprehensive comparisons across use cases. A survey of 600 Japanese participants demonstrates our method's effectiveness, showing how security measures influence attacker's motivation. This approach helps decision-makers customize biometric systems to enhance security while maintaining usability.