  Soft biometrics inference in surveillance scenarios is a topic of interest
for various applications, particularly in security-related areas. However, soft
biometric analysis is not extensively reported in wild conditions. In
particular, previous works on gender recognition report their results in face
datasets, with relatively good image quality and frontal poses. Given the
uncertainty of the availability of the facial region in wild conditions, we
consider that these methods are not adequate for surveillance settings. To
overcome these limitations, we: 1) present frontal and wild face versions of
three well-known surveillance datasets; and 2) propose YinYang-Net (YY-Net), a
model that effectively and dynamically complements facial and body information,
which makes it suitable for gender recognition in wild conditions. The frontal
and wild face datasets derive from widely used Pedestrian Attribute Recognition
(PAR) sets (PETA, PA-100K, and RAP), using a pose-based approach to filter the
frontal samples and facial regions. This approach retrieves the facial region
of images with varying image/subject conditions, where the state-of-the-art
face detectors often fail. YY-Net combines facial and body information through
a learnable fusion matrix and a channel-attention sub-network, focusing on the
most influential body parts according to the specific image/subject features.
We compare it with five PAR methods, consistently obtaining state-of-the-art
results on gender recognition, and reducing the prediction errors by up to 24%
in frontal samples. The announced PAR datasets versions and YY-Net serve as the
basis for wild soft biometrics classification and are available in
https://github.com/Tiago-Roxo.
