  Motivation: In forensic or medico-legal investigation as well as in
anthropology the gender determination of the subject (hit by a disastrous or
any kind of traumatic situation) is mostly the first step. In state-of-the-art
techniques the gender is determined by examining dimensions of the bones of
skull and the pelvis area. In worse situations when there is only a small
portion of the human remains to be investigated and the subject is a child, we
need alternative techniques to determine the gender of the subject. In this
work we propose a technique called GDCNN (Gender Determination with
Convolutional Neural Networks), where the left hand radio-graphs of the
children between a wide range of ages in 1 month to 18 years are examined to
determine the gender. To our knowledge this technique is first of its kind.
Further to identify the area of the attention we used Class Activation Mapping
(CAM). Results: The results suggest the accuracy of the model is as high as
98%, which is very convincing by taking into account the incompletely grown
skeleton of the children. The attention observed with CAM discovers that the
lower part of the hand around carpals (wrist) is more important for child
gender determination.
