  Facial attributes are soft-biometrics that allow limiting the search space,
e.g., by rejecting identities with non-matching facial characteristics such as
nose sizes or eyebrow shapes. In this paper, we investigate how the latest
versions of deep convolutional neural networks, ResNets, perform on the facial
attribute classification task. We test two loss functions: the sigmoid
cross-entropy loss and the Euclidean loss, and find that for classification
performance there is little difference between these two. Using an ensemble of
three ResNets, we obtain the new state-of-the-art facial attribute
classification error of 8.00% on the aligned images of the CelebA dataset. More
significantly, we introduce the Alignment-Free Facial Attribute Classification
Technique (AFFACT), a data augmentation technique that allows a network to
classify facial attributes without requiring alignment beyond detected face
bounding boxes. To our best knowledge, we are the first to report similar
accuracy when using only the detected bounding boxes -- rather than requiring
alignment based on automatically detected facial landmarks -- and who can
improve classification accuracy with rotating and scaling test images. We show
that this approach outperforms the CelebA baseline on unaligned images with a
relative improvement of 36.8%.
