Multi-task Deep Learning for Child Gender and Age Determination on Hand Radiographs.

In anthropology, especially in medico-legal or forensic investigation, the determination of gender and age of the subjects is typically a preliminary and compulsory obligation. State-of-the-art methods for gender determination use dimensions of the bones around the skull and pelvis area. Whereas age is determined on the basis of the degree in which bones have grown, for instance, dental eruption, epiphyseal fusion, tooth mineralization, and diaphyseal length. In this paper, we propose a convolutional neural network model with multi-task learning to determine the gender and age using left-hand radiographs. The model performs well by determining gender and age simultaneously. The results produced by the model specify that there is a relationship between gender and age which is with an increase in age, gender-related features become prominent. Phalanges and Metacarpals are the most significant parts of hand for gender detection based on a certain age group and age detection based on gender respectively. To our knowledge, our method is the first one to determine the gender and age of children simultaneously.