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This inherent relations among multiple face analysis tasks, such as landmarkdetection, head pose estimation, gender recognition and face attributeestimation are crucial to boost the performance of each task, but have not beenthoroughly explored since typically these multiple face analysis tasks arehandled as separate tasks. In this paper, we propose a novel deep multi-taskadversarial learning method to localize facial landmark, estimate head pose andrecognize gender jointly or estimate multiple face attributes simultaneouslythrough exploring their dependencies from both image representation-level andlabel-level. Specifically, the proposed method consists of a deep recognitionnetwork R and a discriminator D. The deep recognition network is used to learnthe shared middle-level image representation and conducts multiple faceanalysis tasks simultaneously. Through multi-task learning mechanism, therecognition network explores the dependencies among multiple face analysistasks, such as facial landmark localization, head pose estimation, genderrecognition and face attribute estimation from image representation-level. Thediscriminator is introduced to enforce the distribution of the multiple faceanalysis tasks to converge to that inherent in the ground-truth labels. Duringtraining, the recognizer tries to confuse the discriminator, while thediscriminator competes with the recognizer through distinguishing the predictedlabel combination from the ground-truth one. Though adversarial learning, weexplore the dependencies among multiple face analysis tasks from label-level.Experimental results on four benchmark databases, i.e., the AFLW database, theMulti-PIE database, the CelebA database and the LFWA database, demonstrate theeffectiveness of the proposed method for multiple face analyses.

Multiple Face Analyses through Adversarial Learning