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Evaluating the accessibility and usability of a universal CAPTCHA based on gestures for smartphones

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Abstract

Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a test used on web sources to differentiate between humans and automated computer robots. CAPTCHA on smartphone platforms is fraught with difficulty for users with visual and hearing impairment. For deaf users, speech as a CAPTCHA input method is not useable in noisy environments. For blind users, typing a letter requires to search for its button on the virtual keyboard; thus, it is time-consuming. Gesture input is therefore a solution for smartphone applications, as neither vision nor hearing is required. This research developed new CAPTCHAs, HearAct and SeeAct, which are suitable for blind, deaf, and sighted users. These CAPTCHAs provide two ways of presenting universally accessible challenges: (1) Users see an image, identify an object in the image and then identify a word that corresponds with that object (SeeAct); (2) users listen to an object’s sound (the “sound-maker”, e.g., a dog) and identify the sound-maker (HearAct). HearAct and SeeAct CAPTCHAs provide both audio and an image of the sound maker, simultaneously. In both, users analyze a word to determine whether the word has a stated letter and then use gestures to answer. If the identified word has the letter, the user must tap; if not, they must swipe. Considering usability and accessibility, as compared to most common audio CAPTCHAs (used by blind users) and text CAPTCHAs (used by deaf and sighted users), results indicate significantly improved accessibility using both HearAct and SeeAct CAPTCHAs. Gesture-based HearAct and SeeAct CAPTCHA have a higher success rate, are more accessible, require less solving time, and are preferred by all users over the traditional CAPTCHA method.

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Acknowledgements

This research was supported by a Grant for Scientific Research from Taif University, Kingdom of Saudi Arabia (Project No. 1-439-6126).

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  1. Department of IT, College of CIT, Taif University, Taif – Al-Haweiah, P. O. Box 888, Taif, ZIP code 21974, Kingdom of Saudi Arabia

    Mrim Alnfiai

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Correspondence to Mrim Alnfiai.

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Alnfiai, M. Evaluating the accessibility and usability of a universal CAPTCHA based on gestures for smartphones. Univ Access Inf Soc 20, 817–831 (2021). https://doi.org/10.1007/s10209-020-00730-x

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