Abstract
Facial attribute recognition is an important and yet challenging research
topic. Different from most previous approaches which predict attributes
only based on the whole images, this paper leverages facial parts
locations for better attribute prediction. A facial abstraction image
which contains both local facial parts and facial texture information
is introduced. This abstraction image is generated by a Generative
Adversarial Network (GAN). Then we build a dual-path facial attribute
recognition network to utilize features from the original face images
and facial abstraction images. Empirically, the features of facial
abstraction images are complementary to features of original face
images. With the facial parts localized by the abstraction images,
our method improves facial attributes recognition, especially the
attributes located on small face regions. Extensive evaluations conducted
on CelebA and LFWA benchmark datasets show that state-of-the-art performance
is achieved.
Keywords
- Computer Vision: Biometrics, Face and Gesture Recognition
- Computer Vision: Computer Vision
- Machine Learning: Deep Learning
