Skull sex identification using improved convolution neural network and least squares method

doi:10.16359/j.cnki.cn11-1963/q.2018.0030

Abstract

Abstract:

Skull sex identification has significant research and applied value in forensic anthropology and skull reconstruction. The traditional skull sex determination methods need expert participation and to some extent, is not objective, because computer-aided methods require to marking landmarks the feature points manually. We present a novel sex determination method based on improved Convolution Neural Network and Least Square. Firstly, obtain multi-angle skull images of three-dimensional skull model, and calculate the probability of each image belongs to male or female. Secondly, the weight of each image is calculated using the Least Squares method based on the probability mean. Lastly, the sex determination function is constructed by using the optimal parameters obtained through the above steps. This method does not need to mark the feature points or do the measurement. Experiments show that the proposed method can get quite a reliable performance with an accuracy of 94.4% for the the complete skull and 87.5% for the incomplete skull.

Key words: Sex determination, Imagery, improved convolution neural network, Least squares method

CLC Number:

Q989

YANG Wen, LIU Xiaoning, LIU Xiongle, ZHU Lipin. Skull sex identification using improved convolution neural network and least squares method[J]. Acta Anthropologica Sinica, 2019, 38(02): 265-275.

Figures/Tables 8

Fig.1 The algorithm flow chart of this paper

Fig.2 Normalized coordinate map

Fig.3 Improved LeNET5 architecture

Fig.4 The learning rate was 0.01 for the classification results

Fig.5 The learning rate was 0.001 for the classification results

Fig.6 The learning rate was 0.01 for the classification results

Fig.7 The learning rate was 0.001 for the classification results

Fig.8 Comparison of sex classification results of skull under different learning rates

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