Statler College of Engineering and Mining Resources
Lane Department of Computer Science and Electrical Engineering
A Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is designed to distinguish humans from machines. Most of the existing tests require reading distorted text embedded in a background image. However, many existing CAPTCHAs are either too difficult for humans due to excessive distortions or are trivial for automated algorithms to solve. These CAPTCHAs also suffer from inherent language as well as alphabet dependencies and are not equally convenient for people of different demographics. Therefore, there is a need to devise other Turing tests which can mitigate these challenges. One such test is matching two faces to establish if they belong to the same individual or not. Utilizing face recognition as the Turing test, we propose FR-CAPTCHA based on finding matching pairs of human faces in an image. We observe that, compared to existing implementations, FR-CAPTCHA achieves a human accuracy of 94% and is robust against automated attacks.
Digital Commons Citation
Goswami, Gaurav; Powell, Brian M.; Vatsa, Mayank; Singh, Richa; and Noore, Afzel, "FR-CAPTCHA: CAPTCHA Based on Recognizing Human Faces" (2014). Faculty & Staff Scholarship. 2543.
Goswami G, Powell BM, Vatsa M, Singh R, Noore A (2014) FR-CAPTCHA: CAPTCHA Based on Recognizing Human Faces. PLoS ONE 9(4): e91708. https://doi.org/10.1371/journal.pone.0091708