  In person attributes recognition, we describe a person in terms of their
appearance. Typically, this includes a wide range of traits including age,
gender, clothing, and footwear. Although this could be used in a wide variety
of scenarios, it generally is applied to video surveillance, where attribute
recognition is impacted by low resolution, and other issues such as variable
pose, occlusion and shadow. Recent approaches have used deep convolutional
neural networks (CNNs) to improve the accuracy in person attribute recognition.
However, many of these networks are relatively shallow and it is unclear to
what extent they use contextual cues to improve classification accuracy. In
this paper, we propose deeper methods for person attribute recognition.
Interpreting the reasons behind the classification is highly important, as it
can provide insight into how the classifier is making decisions. Interpretation
suggests that deeper networks generally take more contextual information into
consideration, which helps improve classification accuracy and
generalizability. We present experimental analysis and results for whole body
attributes using the PA-100K and PETA datasets and facial attributes using the
CelebA dataset.
