  Face attribute estimation has many potential applications in video
surveillance, face retrieval, and social media. While a number of methods have
been proposed for face attribute estimation, most of them did not explicitly
consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal
and holistic vs. local) during feature representation learning. In this paper,
we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate
multiple heterogeneous attributes from a single face image. In DMTL, we tackle
attribute correlation and heterogeneity with convolutional neural networks
(CNNs) consisting of shared feature learning for all the attributes, and
category-specific feature learning for heterogeneous attributes. We also
introduce an unconstrained face database (LFW+), an extension of public-domain
LFW, with heterogeneous demographic attributes (age, gender, and race) obtained
via crowdsourcing. Experimental results on benchmarks with multiple face
attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed
approach has superior performance compared to state of the art. Finally,
evaluations on a public-domain face database (LAP) with a single attribute show
that the proposed approach has excellent generalization ability.
