A novel deep neural network for identification of sex and ethnicity based on unknown skulls

Abstract

The determination of the sex and ethnicity is crucial in the identification of unknown human remains in biology and forensic science. The components of these two biological traits can be effectively evaluated using the skull, which makes it one of the most essential structures for the aforementioned purpose. However, performing simultaneous determination of sex and ethnicity remains a challenge in the identification of unknown humans. In this study, a multi-attribute recognition framework for unknown skulls, which integrates multitask and multiview cross-attention, is proposed. Multi-angle images of the skull first serve as input to a parallel convolutional neural network, yielding its independent view features. To increase the performance of the skull multi-attribute recognition, a view cross-attention mechanism is then introduced. This mechanism uses the independent view features of the skull to obtain global view features. Afterwards, the final output structure is divided into two branches, one used to identify the gender of the skull and the other to identify its ethnicity. The experiment involves 214 samples that consist of 79 samples (41 males and 38 females) from the Han Chinese population in northern China and 135 samples (60 males and 75 females) from the Uyghur population in Xinjiang, China. The results of the experiment demonstrate that the optimal performance of the skull multi-attribute recognition model is obtained when ResNet18 is used as a feature-sharing network. The gender and ethnic identifications for the skull have accuracies of 95.94 % and 98.45 %, respectively. This verifies that the proposed method has high accuracy and generalization ability.