A Deep Cascade Network for Unaligned Face Attribute Classification

Understand

Humans focus attention on different face regions when recognizing face attributes.

• Most existing face attribute classification methods use the whole image as input.
• Moreover, some of these methods rely on fiducial landmarks to provide defined face parts.
• In this paper, we propose a cascade network that simultaneously learns to localize face regions specific to attributes and performs attribute classification without alignment.