We consider the task of predicting various traits of\u00a0a person given an image of their face. We estimate\u00a0both objective traits, such as gender, ethnicity\u00a0and hair-color; as well as subjective traits, such\u00a0as the emotion a person expresses or whether he is\u00a0humorous or attractive. For sizeable experimentation,\u00a0we contribute a new Face Attributes Dataset (FAD), having roughly 200,000 attribute labels for\u00a0the above traits, for over 10,000 facial images.\u00a0Due to the recent surge of research on Deep Convolutional\u00a0Neural Networks (CNNs), we begin by\u00a0using a CNN architecture for estimating facial attributes\u00a0and show that they indeed provide an impressive\u00a0baseline performance. To further improve\u00a0performance, we propose a novel approach that incorporates\u00a0facial landmark information for input images as an additional channel, helping the CNN\u00a0learn better attribute-specific features so that the\u00a0landmarks across various training images hold correspondence.\u00a0We empirically analyse the performance\u00a0of our method, showing consistent improvement\u00a0over the baseline across traits.<\/p>\n","protected":false},"excerpt":{"rendered":"
We consider the task of predicting various traits of\u00a0a person given an image of their face. We estimate\u00a0both objective traits, such as gender, ethnicity\u00a0and hair-color; as well as subjective traits, such\u00a0as the emotion a person expresses or whether he is\u00a0humorous or attractive. For sizeable experimentation,\u00a0we contribute a new Face Attributes Dataset (FAD), having roughly 200,000 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