  The great success of Convolutional Neural Networks (CNN) for facial attribute
prediction relies on a large amount of labeled images. Facial image datasets
are usually annotated by some commonly used attributes (e.g., gender), while
labels for the other attributes (e.g., big nose) are limited which causes their
prediction challenging. To address this problem, we use a new Multi-Task
Learning (MTL) paradigm in which a facial attribute predictor uses the
knowledge of other related attributes to obtain a better generalization
performance. Here, we leverage MLT paradigm in two problem settings. First, it
is assumed that the structure of the tasks (e.g., grouping pattern of facial
attributes) is known as a prior knowledge, and parameters of the tasks (i.e.,
predictors) within the same group are represented by a linear combination of a
limited number of underlying basis tasks. Here, a sparsity constraint on the
coefficients of this linear combination is also considered such that each task
is represented in a more structured and simpler manner. Second, it is assumed
that the structure of the tasks is unknown, and then structure and parameters
of the tasks are learned jointly by using a Laplacian regularization framework.
Our MTL methods are compared with competing methods for facial attribute
prediction to show its effectiveness.
