  This inherent relations among multiple face analysis tasks, such as landmark
detection, head pose estimation, gender recognition and face attribute
estimation are crucial to boost the performance of each task, but have not been
thoroughly explored since typically these multiple face analysis tasks are
handled as separate tasks. In this paper, we propose a novel deep multi-task
adversarial learning method to localize facial landmark, estimate head pose and
recognize gender jointly or estimate multiple face attributes simultaneously
through exploring their dependencies from both image representation-level and
label-level. Specifically, the proposed method consists of a deep recognition
network R and a discriminator D. The deep recognition network is used to learn
the shared middle-level image representation and conducts multiple face
analysis tasks simultaneously. Through multi-task learning mechanism, the
recognition network explores the dependencies among multiple face analysis
tasks, such as facial landmark localization, head pose estimation, gender
recognition and face attribute estimation from image representation-level. The
discriminator is introduced to enforce the distribution of the multiple face
analysis tasks to converge to that inherent in the ground-truth labels. During
training, the recognizer tries to confuse the discriminator, while the
discriminator competes with the recognizer through distinguishing the predicted
label combination from the ground-truth one. Though adversarial learning, we
explore the dependencies among multiple face analysis tasks from label-level.
Experimental results on four benchmark databases, i.e., the AFLW database, the
Multi-PIE database, the CelebA database and the LFWA database, demonstrate the
effectiveness of the proposed method for multiple face analyses.
