北京航空航天大学学报 ›› 2021, Vol. 47 ›› Issue (3): 641-649.doi: 10.13700/j.bh.1001-5965.2020.0446

• 论文 • 上一篇    下一篇

结合颅骨形态特征与神经网络的民族判别

孙慧杰1, 赵俊莉1, 郑鑫1, 热孜万古丽·夏米西丁2, 李奕1, 周明全3   

  1. 1. 青岛大学 数据科学与软件工程学院, 青岛 266071;
    2. 新疆师范大学 计算机科学技术学院, 乌鲁木齐 830054;
    3. 教育部虚拟现实应用工程研究中心, 北京 100875
  • 收稿日期:2020-08-24 发布日期:2021-04-08
  • 通讯作者: 赵俊莉 E-mail:zhaojl@yeah.net
  • 作者简介:孙慧杰,女,硕士研究生。主要研究方向:计算机图形学;赵俊莉,女,博士,副教授,硕士生导师。主要研究方向:计算机图形学、计算机视觉、虚拟现实。
  • 基金资助:
    国家自然科学基金(61702293);全国统计科学研究项目(2020355);教育部虚拟现实应用工程研究中心基金项目(MEOBNUEVRA201601);中国博士后科学基金(2017M622137);山东省重点研发计划重大科技创新工程(2019JZZY020101)

Ethnic identification by combining features of skull morphology with neural network

SUN Huijie1, ZHAO Junli1, ZHENG Xin1, REZIWANGULI Xiamixiding2, LI Yi1, ZHOU Mingquan3   

  1. 1. School of Data Science and Software Engineering, Qingdao University, Qingdao 266071, China;
    2. School of Computer Science and Technology, Xinjiang Normal University, Urumqi 830054, China;
    3. Virtual Reality Research Center of Ministry of Education, Beijing 100875, China
  • Received:2020-08-24 Published:2021-04-08
  • Supported by:
    National Natural Science Foundation of China (61702293); National Statistical Science Research Project (2020355); the Open Research Fund of the Ministry of Education for Engineering Research Center of Virtual Reality Application (MEOBNUEVRA201601); China Postdoctoral Science Foundation (2017M622137); Key Research and Development Plan-Major Scientific and Technological Innovation Projects of ShanDong Province (2019JZZY020101)

摘要: 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。

关键词: 颅骨形态特征, 反向传播神经网络(BPNN), 颅骨民族判别, 机器学习, Adam算法

Abstract: 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.

Key words: features of skull morphology, Back-Propagation Neural Network (BPNN), skull ethnic identification, machine learning, Adam algorithm

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