  Soft biometric attributes such as gender, ethnicity or age may provide useful
information for biometrics and forensics applications. Researchers used, e.g.,
face, gait, iris, and hand, etc. to classify such attributes. Even though hand
has been widely studied for biometric recognition, relatively less attention
has been given to soft biometrics from hand. Previous studies of soft
biometrics based on hand images focused on gender and well-controlled imaging
environment. In this paper, the gender and ethnicity classification in
uncontrolled environment are considered. Gender and ethnicity labels are
collected and provided for subjects in a publicly available database, which
contains hand images from the Internet. Five deep learning models are
fine-tuned and evaluated in gender and ethnicity classification scenarios based
on palmar 1) full hand, 2) segmented hand and 3) palmprint images. The
experimental results indicate that for gender and ethnicity classification in
uncontrolled environment, full and segmented hand images are more suitable than
palmprint images.
