颅骨性别鉴定在法医学和颅骨面貌复原等领域具有重要研究意义和应用价值,针对传统颅骨性别鉴定需要专家参与且主观性强、计算机辅助方法需要人工标定特征点等问题,本文提出了结合改进卷积神经网络和最小二乘法的颅骨性别鉴定方法。首先,获取三维颅骨模型多角度颅骨图像,利用改进的卷积神经网络计算每个样本的每张图像属于男性和女性的概率;其次,基于概率均值采用最小二乘法计算每张图像对性别鉴定的权重;最后,利用上述步骤得到的最优参数构造决策函数,通过决策值完成颅骨性别鉴定。本文方法抛弃了繁琐的手动测量,对完整颅骨的性别鉴定正确率高达94.4%,对不完整颅骨的性别鉴定正确率高达87.5%,能够获得较好的颅骨性别鉴定性能。

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.