  Representing the spatial properties of facial attributes is a vital challenge
for facial attribute recognition (FAR). Recent advances have achieved the
reliable performances for FAR, benefiting from the description of spatial
properties via extra prior information. However, the extra prior information
might not be always available, resulting in the restricted application scenario
of the prior-based methods. Meanwhile, the spatial ambiguity of facial
attributes caused by inherent spatial diversities of facial parts is ignored.
To address these issues, we propose a prior-free method for attribute spatial
decomposition (ASD), mitigating the spatial ambiguity of facial attributes
without any extra prior information. Specifically, assignment-embedding module
(AEM) is proposed to enable the procedure of ASD, which consists of two
operations: attribute-to-location assignment and location-to-attribute
embedding. The attribute-to-location assignment first decomposes the feature
map based on latent factors, assigning the magnitude of attribute components on
each spatial location. Then, the assigned attribute components from all
locations to represent the global-level attribute embeddings. Furthermore,
correlation matrix minimization (CMM) is introduced to enlarge the
discriminability of attribute embeddings. Experimental results demonstrate the
superiority of ASD compared with state-of-the-art prior-based methods, while
the reliable performance of ASD for the case of limited training data is
further validated.
