  Facial attribute recognition is conventionally computed from a single image.
In practice, each subject may have multiple face images. Taking the eye size as
an example, it should not change, but it may have different estimation in
multiple images, which would make a negative impact on face recognition. Thus,
how to compute these attributes corresponding to each subject rather than each
single image is a profound work. To address this question, we deploy deep
training for facial attributes prediction, and we explore the inconsistency
issue among the attributes computed from each single image. Then, we develop
two approaches to address the inconsistency issue. Experimental results show
that the proposed methods can handle facial attribute estimation on either
multiple still images or video frames, and can correct the incorrectly
annotated labels. The experiments are conducted on two large public databases
with annotations of facial attributes.
