With data theft and computer break-ins becoming increasingly common, there is a great need for secondary authentication to reduce automated attacks while posing a minimal hindrance to legitimate users. CAPTCHA is one of the possible ways to classify human users and automated scripts. Though text-based CAPTCHAs are used in many applications, they pose a challenge due to language dependency. In this paper, we propose a face image-based CAPTCHA as a potential solution. To solve the CAPTCHA, users must correctly identify visually-distorted human faces embedded in a complex background without selecting any non-human faces. The proposed algorithm generates a CAPTCHA that offers better human accuracy and lower machine attack rates compared to existing approaches. © 2013 Elsevier B.V. All rights reserved.