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Facial attributes are soft-biometrics that allow limiting the search space,e.g., by rejecting identities with non-matching facial characteristics such asnose sizes or eyebrow shapes. In this paper, we investigate how the latestversions of deep convolutional neural networks, ResNets, perform on the facialattribute classification task. We test two loss functions: the sigmoidcross-entropy loss and the Euclidean loss, and find that for classificationperformance there is little difference between these two. Using an ensemble ofthree ResNets, we obtain the new state-of-the-art facial attributeclassification error of 8.00% on the aligned images of the CelebA dataset. Moresignificantly, we introduce the Alignment-Free Facial Attribute ClassificationTechnique (AFFACT), a data augmentation technique that allows a network toclassify facial attributes without requiring alignment beyond detected facebounding boxes. To our best knowledge, we are the first to report similaraccuracy when using only the detected bounding boxes -- rather than requiringalignment based on automatically detected facial landmarks -- and who canimprove classification accuracy with rotating and scaling test images. We showthat this approach outperforms the CelebA baseline on unaligned images with arelative improvement of 36.8%.

AFFACT - Alignment-Free Facial Attribute Classification Technique