  Recently, facial attribute classification (FAC) has attracted significant
attention in the computer vision community. Great progress has been made along
with the availability of challenging FAC datasets. However, conventional FAC
methods usually firstly pre-process the input images (i.e., perform face
detection and alignment) and then predict facial attributes. These methods
ignore the inherent dependencies among these tasks (i.e., face detection,
facial landmark localization and FAC). Moreover, some methods using
convolutional neural network are trained based on the fixed loss weights
without considering the differences between facial attributes. In order to
address the above problems, we propose a novel multi-task learning of cas-
caded convolutional neural network method, termed MCFA, for predicting multiple
facial attributes simultaneously. Specifically, the proposed method takes
advantage of three cascaded sub-networks (i.e., S_Net, M_Net and L_Net
corresponding to the neural networks under different scales) to jointly train
multiple tasks in a coarse-to-fine manner, which can achieve end-to-end
optimization. Furthermore, the proposed method automatically assigns the loss
weight to each facial attribute based on a novel dynamic weighting scheme, thus
making the proposed method concentrate on predicting the more difficult facial
attributes. Experimental results show that the proposed method outperforms
several state-of-the-art FAC methods on the challenging CelebA and LFWA
datasets.
