  Soft biometrics analysis is seen as an important research topic, given its
relevance to various applications. However, even though it is frequently seen
as a solved task, it can still be very hard to perform in wild conditions,
under varying image conditions, uncooperative poses, and occlusions.
Considering the gender trait as our topic of study, we report an extensive
analysis of the feasibility of its inference regarding image (resolution,
luminosity, and blurriness) and subject-based features (face and body keypoints
confidence). Using three state-of-the-art datasets (PETA, PA-100K, RAP) and
five Person Attribute Recognition models, we correlate feature analysis with
gender inference accuracy using the Shapley value, enabling us to perceive the
importance of each image/subject-based feature. Furthermore, we analyze
face-based gender inference and assess the pose effect on it. Our results
suggest that: 1) image-based features are more influential for low-quality
data; 2) an increase in image quality translates into higher subject-based
feature importance; 3) face-based gender inference accuracy correlates with
image quality increase; and 4) subjects' frontal pose promotes an implicit
attention towards the face. The reported results are seen as a basis for
subsequent developments of inference approaches in uncontrolled outdoor
environments, which typically correspond to visual surveillance conditions.
