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首页> 外文期刊>Multimedia Tools and Applications >FaceCAPTCHA: a CAPTCHA that identifies the gender of face images unrecognized by existing gender classifiers
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FaceCAPTCHA: a CAPTCHA that identifies the gender of face images unrecognized by existing gender classifiers

机译：FaceCAPTCHA：一种验证码，用于识别现有性别分类器无法识别的面部图像的性别

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摘要

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.
机译：计算机往往无法按性别对人脸进行分类，尤其是在视点发生变化或遮挡后，这使得提取必要的图像特征更加困难。相反，人类擅长识别性别，但是在处理大量图像时有困难。考虑到这一差距，我们提出了FaceCAPTCHA，这是一种基于图像的新型验证码，要求用户识别其性别无法被计算机识别的面部图像的性别（无法区分性别的面部）。通过将手动的性别分类任务转换为CAPTCHA测试，FaceCAPTCHA不仅可以连续识别无法区分性别的面孔的性别，而且可以区分人和计算机并生成新的测试图像。我们的用户研究表明，FaceCAPTCHA可以可靠地识别出性别区分的面孔。平均八张图像的FaceCAPTCHA测试平均在12.41 s内完成，人类成功率为86.51％，可以通过过滤容易出错的测试图像来进一步提高该成功率。相反，通过随机猜测通过FaceCAPTCHA测试的可能性为0.006％。因此，我们可以得出结论，FaceCAPTCHA具有强大的抵御恶意攻击能力，并且易于实际使用。

著录项

  • 来源
    《Multimedia Tools and Applications》 |2014年第2期|1215-1237|共23页
  • 作者

    Jonghak Kim; Sangtae Kim; Joonhyuk Yang; Jung-hee Ryu; KwangYun Wohn;

  • 作者单位

    Graduate School of Culture Technology, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701,Republic of Korea;

    Graduate School of Culture Technology, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701,Republic of Korea;

    Graduate School of Culture Technology, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701,Republic of Korea;

    Olaworks, Inc., 11th Fl. Paradise Venture Bldg., 708-33, Yeoksam-dong, Gangnam-gu, Seoul,Republic of Korea;

    Graduate School of Culture Technology, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701,Republic of Korea;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    CAPTCHA; Crowdsourcing; Gender classification; Human computation; Image tagging; Web application;

    机译：验证码;众包;性别分类;人为计算;图像标记;Web应用程序;

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