  Face images contain a wide variety of attribute information. In this paper,
we propose a generalized framework for joint estimation of ordinal and nominal
attributes based on information sharing. We tackle the correlation problem
between heterogeneous attributes using hard parameter sharing of shallow
features, and trade-off multiple loss functions by considering homoskedastic
uncertainty for each attribute estimation task. This leads to optimal
estimation of multiple attributes of the face and reduces the training cost of
multitask learning. Experimental results on benchmarks with multiple face
attributes show that the proposed approach has superior performance compared to
state of the art. Finally, we discuss the bias issues arising from the proposed
approach in face attribute estimation and validate its feasibility on edge
systems.
