Abstract
Facial beauty prediction (FBP) aims to develop a
machine that automatically makes facial attractiveness assessment. To a large extent, the perception of facial beauty for a human is involved with
the attributes of facial appearance, which provides
some significant visual cues for FBP. Deep convolution neural networks (CNNs) have shown its
power for FBP, but convolution filters with fixed
parameters cannot take full advantage of the facial attributes for FBP. To address this problem, we
propose an Attribute-aware Convolutional Neural
Network (AaNet) that modulates the filters of the
main network, adaptively, using parameter generators that take beauty-related attributes as extra inputs. The parameter generators update the filters in
the main network in two different manners: filter
tuning or filter rebirth. However, AaNet takes attributes information as prior knowledge, that is illsuited to those datasets merely with task-oriented
labels. Therefore, imitating the design of AaNet,
we further propose a Pseudo Attribute-aware Convolutional Neural Network (P-AaNet) that modulates filters conditioned on global context embeddings (pseudo attributes) of input faces learnt by
a lightweight pseudo attribute distiller. Extensive
ablation studies show that the AaNet and P-AaNet
improve the performance of FBP when compared
to conventional convolution and attention scheme,
which validates the effectiveness of our method