We present a method using facial attributes for continuous authentication of smartphone users. The binary attribute classifiers are trained using PubFig dataset and provide compact visual descriptions of faces. The learned classifiers are applied to the image of the current user of a mobile device to extract the attributes and then authentication is done by simply comparing the difference between the acquired attributes and the enrolled attributes of the original user. Extensive experiments on two publicly available unconstrained mobile face video datasets show that our method is able to capture meaningful attributes of faces and performs better than the previously proposed LBP-based authentication method.