'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
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: i) manual estimation of soft biometrics, and
ii) automatic estimation from two Commercial Off-The-Shelf
systems (COTS). All experiments are reported using the 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 scoresThis work was funded by Spanish Guardia Civil and project CogniMetrics (TEC2015-70627-R) from MINECO/FEDE