Ancestry Estimation of Skull in Chinese Population Based on Improved Convolutional Neural Network

Wen Yang, Mingquan Zhou, Pengyue Lin, Guohua Geng, Xiaoning Liu, Kang Li

Abstract: The estimation of ancestry is an essential benchmark for positive identification of heavily decomposed bodies that are recovered in a variety of death and crime scenes. Aiming at the problem of skull ancestry estimation, this paper proposes an improved convolutional neural network method to realize ancestry estimation. We use the six-angle images of the skull as the input of the network. By improving the basic model LeNet5 of the convolutional neural network, we preserve the depth semantics and content information of the image, reduce the number of parameters, and ensure the learning ability of network features. In the experiment, 156 yellow skulls from northern China and 178 white skulls from Xinjiang were used as subjects, 80% of skull samples were used as training sets and 20% as test sets. Experiments on the training set and test set show that the improved CNN network architecture achieves 95.88% accuracy on the training set and 95.52% accuracy on the test set. In addition, we also designed experiments on the contribution of various parts of the skull to ancestor identification. The experimental results show that each region of the skull is useful for ancestor identification, but the effect is different. Compared with other networks, the network structure of this paper has the highest accuracy and better performance.