  The role of soft biometrics to enhance person recognition systems in
unconstrained scenarios has not been extensively studied. Here, we explore the
utility of the following modalities: gender, ethnicity, age, glasses, beard,
and moustache. We consider two assumptions: 1) manual estimation of soft
biometrics and 2) automatic estimation from two commercial off-the-shelf
systems (COTS). All experiments are reported using the labeled faces in the
wild (LFW) database. First, we study the discrimination capabilities of soft
biometrics standalone. Then, experiments are carried out fusing soft biometrics
with two state-of-the-art face recognition systems based on deep learning. We
observe that soft biometrics is a valuable complement to the face modality in
unconstrained scenarios, with relative improvements up to 40%/15% in the
verification performance when using manual/automatic soft biometrics
estimation. Results are reproducible as we make public our manual annotations
and COTS outputs of soft biometrics over LFW, as well as the face recognition
scores.
