  We consider the task of predicting various traits of a person given an image
of their face. We estimate both objective traits, such as gender, ethnicity and
hair-color; as well as subjective traits, such as the emotion a person
expresses or whether he is humorous or attractive. For sizeable
experimentation, we contribute a new Face Attributes Dataset (FAD), having
roughly 200,000 attribute labels for the above traits, for over 10,000 facial
images. Due to the recent surge of research on Deep Convolutional Neural
Networks (CNNs), we begin by using a CNN architecture for estimating facial
attributes and show that they indeed provide an impressive baseline
performance. To further improve performance, we propose a novel approach that
incorporates facial landmark information for input images as an additional
channel, helping the CNN learn better attribute-specific features so that the
landmarks across various training images hold correspondence. We empirically
analyse the performance of our method, showing consistent improvement over the
baseline across traits.
