口腔医学 ›› 2023, Vol. 43 ›› Issue (12): 1057-1064.doi: 10.13591/j.cnki.kqyx.2023.12.001

• 基础研究 •    下一篇

基于人工智能技术的面相照片标志点自动定位的评价

邱韬1,何涛2,张强2,肖雨璇1,郭维华1,3()   

  1. 1 口腔疾病研究国家重点实验室,国家口腔疾病临床医学研究中心,四川大学华西口腔医院儿童口腔科,四川成都(610041)
    2 四川大学计算机学院,四川成都(610065)
    3 云南省口腔医学重点实验室,昆明医科大学附属口腔医院儿童口腔科,云南昆明(650500)
  • 修回日期:2023-08-01 出版日期:2023-12-28 发布日期:2023-12-28
  • 通讯作者: 郭维华 E-mail:guoweihua943019@163.com
  • 基金资助:
    国家自然科学基金(82270958);国家自然科学基金(31971281);四川省科技计划(科技创新人才项目)(2022JDRC0043);四川大学华西口腔医院研发项目(RD-03-202106)

Automated photogrammetric analysis from a 2D photograph by convolutional neural network

QIU Tao1,HE Tao2,ZHANG Qiang2,XIAO Yuxuan1,GUO Weihua1,3()   

  1. State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Pediatric Dentistry, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
  • Revised:2023-08-01 Online:2023-12-28 Published:2023-12-28
  • Contact: GUO Weihua E-mail:guoweihua943019@163.com

摘要:

目的 本研究旨在构建一种基于卷积神经网络(CNN)的面相自动定点分析系统及其准确性评价体系。方法 收集467例6~55岁患者的正侧貌照片,选取常用标志点(正貌45个,侧貌31个)及相关比例、角度,构建基于卷积神经网络(全局→局部模型)的自动化面相定点分析系统,提出了基于审美考虑的准确性评价体系,即定点的标准化平均误差(NME)、单位距离内的定点成功率(SDR)及测量指标的成功分类率(SCR)。结果 测试集NME为0.079±0.221(侧貌),0.025±0.021(正貌)。测试集0.02、0.04、0.06、0.08、0.10单位距离内的SDR分别为54.17%、85.71%、93.94%、96.69%、97.37%(侧貌);58.54%、87.59%、95.64%、98.03%、99.00%(正貌)。测试集中多数角和比例等测量指标的SCR为100%。结论 本研究成功构建了基于CNN的面相自动定点分析系统,该系统可在20 s内完成76个标志点的精准检测,并提出了基于审美考量的面相自动化定点分析的准确性评价体系,简化了面相测量分析的过程。

关键词: 面相测量分析, 卷积神经网络, 面部美学

Abstract:

Objective To develop an automatic system to simplify the progress based on convolutional neural networks and build its accuracy evaluation system. Methods A total of 467 lateral and frontal views (age range: 6-55 years) were collected. Forty-five landmarks were detected in the front view and so as 31 in the profile. An automatic locating system based on CNN was developed, consisting of a global detection module and a local correction module. An accuracy evaluation system based on aesthetic considerations was proposed, which consisted of the standardized average error (NME) of the detected points, success rate (SDR) of landmark locating within the unit distance and the successful classification rate (SCR). Results The NME of our test set was 0.079±0.221 in profile and 0.025±0.021 in frontage.The SDR of 0.02, 0.04, 0.06, 0.08 and 0.10 units were respectively 54.17%, 85.71%, 93.94%, 96.69%, and 97.37% in profile, 58.54%、87.59%、95.64%、98.03%、99.00% in frontage. Most of their SCR of our test set were 100%. Conclusion In this study, we successfully proposed an automatic landmark locating system based on CNN. The system can detect 76 landmarks with high detection accuracy within 20 seconds. Moreover, we constructed an evaluation metric of the automatic landmark locating system which focused on the facial aesthetics. Both the location and evaluation system can highly simplify the photogrammetric analysis.

Key words: photogrammetric analysis, convolutional neural networks, facial aesthetics

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