FaceCAPTCHA: a CAPTCHA that identifies the gender of face images unrecognized by existing gender classifiers

Cited 6 time in webofscience Cited 0 time in scopus
  • Hit : 336
  • Download : 0
Computers tend to fail to classify human faces by gender, especially upon changes in viewpoint or upon occlusion that make it more difficult to extract the necessary image features. In contrast, humans are good at identifying gender but have difficulties in dealing with a large number of images. Accounting for this gap, we proposed FaceCAPTCHA, a novel image-based CAPTCHA that asks users to identify the gender of face images whose gender cannot be recognized by computers (gender-indiscernible faces). By converting the manual gender classification task into a CAPTCHA test, FaceCAPTCHA was designed to not only continuously identify the gender of gender-indiscernible faces but also differentiate between humans and computers and generate new test images. Our user studies showed that FaceCAPTCHA reliably identifies gender-indiscernible faces. A single eight-image FaceCAPTCHA test was completed in 12.41 s on average with a human success rate of 86.51 %, which can be further increased by filtering error-prone test images. In contrast, the probability of passing a FaceCAPTCHA test by random guessing was 0.006 %. We could therefore conclude that FaceCAPTCHA is robust against malicious attacks and easy enough for practical use.
Publisher
SPRINGER
Issue Date
2014-09
Language
English
Article Type
Article
Keywords

CLASSIFICATION; RECOGNITION; SECURITY

Citation

MULTIMEDIA TOOLS AND APPLICATIONS, v.72, no.2, pp.1215 - 1237

ISSN
1380-7501
DOI
10.1007/s11042-013-1422-z
URI
http://hdl.handle.net/10203/192825
Appears in Collection
GCT-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 6 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0