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Facial Landmark Detection by Deep Multi-task Learning
Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaoou Tang
Dept. of Information Engineering, The Chinese University of Hong Kong, Hong Kong, ChinaAbstract. Facial landmark detection has long been impeded by the problems of occlusion and pose variation. Instead of treating the detection task as a single and independent problem, we investigate the possibility of improving detection robustness through multi-task learning. Specifically, we wish to optimize facial landmark detection together with heterogeneous but subtly correlated tasks, e.g. head pose estimation and facial attribute inference. This is non-trivial since different tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, with task-wise early stopping to facilitate learning convergence. Extensive evaluations show that the proposed task-constrained learning (i) outperforms existing methods, especially in dealing with faces with severe occlusion and pose variation, and (ii) reduces model complexity drastically compared to the state-of-the-art method based on cascaded deep model [21].
LNCS 8694, p. 94 ff.
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© Springer International Publishing Switzerland 2014