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.