Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression
Recognition in the Wild
Abstract
Past research on facial expressions have used relatively limited datasets, which makes it unclear whether current methods can be employed in real world. In this paper, we present a novel database, RAF-DB, which contains
about 30000 facial images from thousands of individuals.
Each image has been individually labeled about 40 times,
then EM algorithm was used to filter out unreliable labels. Crowdsourcing reveals that real-world faces often express compound emotions, or even mixture ones. For all
we know, RAF-DB is the first database that contains compound expressions in the wild. Our cross-database study
shows that the action units of basic emotions in RAF-DB are
much more diverse than, or even deviate from, those of labcontrolled ones. To address this problem, we propose a new
DLP-CNN (Deep Locality-Preserving CNN) method, which
aims to enhance the discriminative power of deep features
by preserving the locality closeness while maximizing the
inter-class scatters. The benchmark experiments on the 7-
class basic expressions and 11-class compound expressions, as well as the additional experiments on SFEW and CK+
databases, show that the proposed DLP-CNN outperforms
the state-of-the-art handcrafted features and deep learning
based methods for the expression recognition in the wild.