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In practice, each subject may have multiple face images. To address this question, we deploy deep training for facial attributes prediction.
Abstract— Facial attribute recognition is conventionally com- puted from a single image. In practice, each subject may have multiple face images. Taking the eye.
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Description:For one single person, all the photographs were taken on the same day, although the subject was forced to stand up and sit down again in order to change pose and gesture. These scans are not included in the training set of the BFM and form a standardized test set with a ground truth for pose and illumination. In addition, the dataset includes 6 manually labeled landmark positions for every face: Other information, such as gender, year of birth, ethnicity, glasses whether a person wears glasses or not and the time of each session are also available.