结合颅骨形态特征与神经网络的民族判别

针对颅骨民族判别问题，提出结合颅骨形态特征与神经网络的判别方法，可以推进法医人类学的发展，加快探索民族发展历程。首先，根据颅骨形态学相关研究，提取36个维吾尔族和汉族颅骨数据的几何特征；其次，采用反向传播神经网络（BPNN）对特征向量进行民族判别，并通过Adam算法对网络进行优化，避免陷入局部最优值，添加正则化项保证算法稳定性；最后，分别采用2种网络结构进行对比实验，输入层、隐藏层和输出层的神经元个数分别为36、6、2和36、12、2，并设置不同初始学习率进行对比实验。结果表明:隐藏层神经元个数为12、学习率为0.000 1时，分类精度最高，测试阶段平均准确率最高为97.5%。为了验证所提方法的普适性，生成116例国外颅骨数据进行实验，测试阶段平均准确率为90.96%。相比较于支持向量机（SVM）、决策树、KNN、Fisher等机器学习方法，所提方法学习能力更强且分类精度有明显提升。 

Ethnic identification by combining features of skull morphology with neural network

China is a multi-ethnic country. It is of great significance for the skull identification to realize the skull ethnic identification through computers, which can promote the development of forensic anthropology and exploration of national development. Firstly, according to the skull morphology studies, 36 Uighur and Han geometric features of the skull data are extracted, and the Back-Propagation Neural Network (BPNN) of feature vectors is used for ethnic identification. In order to optimize the network, Adam algorithm is adopted to avoid falling into local minimum, and to ensure the stability of the algorithm with regularization terms. Two network structures are used for comparative experiments. The number of neurons in the input layer, hidden layer and output layer are 36, 6, 2 and 36, 12, 2, respectively, and different initial learning rates are set for comparative experiments. The results show that, when the number of hidden-layer neurons is 12 and the learning rate is 0.000 1, the classification accuracy is the highest and the highest accuracy rate in the test stage is 97.5%. In order to verify the universality of the method in this paper, 116 foreign skull data are generated for experiments, and the accuracy rate of the test stage is 90.96%. Compared with machine learning methods such as Support Vector Machine (SVM), decision-making tree, KNN, and Fisher, the proposed method has stronger learning ability and significantly improved classification accuracy.