Skull identification based on least square canonical correlation analysis

被引：0
|
作者：
Zhou M.-Q. [1 ,2 ]
Yang W. [1 ]
Lin P.-Y. [1 ]
Geng G.-H. [1 ]
Liu X.-N. [1 ]
Li K. [1 ]
机构：
[1] College of Information Science and Technology, Northwest University, Xi'an
[2] College of Information Science and Technology, Beijing Normal University, Beijing
来源：
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2021年 / 29卷 / 01期
关键词：
Image reconstruction; Least square canonical dependency analysis; Main related information; Skull recognition; Statistical shape model;
D O I：
10.37188/OPE.20212901.0201
中图分类号：
学科分类号：
摘要：
To measure the correlation between the skull and the face reliably and improve the skull recognition ability, a skull identification method based on least squares canonical dependency analysis (LSCDA) was proposed. First, a statistical shape model of the skull and facial skin was constructed, and the high-dimensional skull and facial skin were mapped to the low-dimensional shape parameter space. Second, the main relevant information about the skull and facial skin was extracted based on LSCDA, and an overall correlation analysis model was constructed to measure the relationship between the skull and the skin. By considering the difference in the correlations between different regions of the skull, the skull was divided into five regions: the forehead, eyes, nose, mouth, and contour. Based on LSCDA, the main relevant information about the skull and facial skin was extracted and constructed by region. A regional correlation analysis model was used to measure the local detail correlation between the skull and the face. Finally, a global correlation analysis model and a regional correlation analysis model were used to measure the matching relationship between the skull and facial skin, and the matching score between each face in the skull and facial skin database was calculated. The face with the highest matching score yielded the correct recognition result for achieving skull identification. The experimental results reveal that the recognition accuracy of the overall correlation analysis model is 85.2%. The recognition accuracy of the contour region in the single region correlation analysis model is the highest, whereas that of the nose region is the lowest. The correlation analysis established by fusing the five regional models indicates that the recognition accuracy rate is as high as 95.2% and that the method based on regional fusion is better than the overall correlation analysis method. © 2021, Science Press. All right reserved.
引用
收藏
页码：201 / 210
页数：9
相关论文
共 23 条
  • [1] BRINKMANN B., Forensic anthropology, International Journal of Legal Medicine, 121, 6, pp. 431-432, (2007)
  • [2] DAMAS S, CORDONOSCAR, IBANEZOSCAR, Et al., Forensic identification by computer-aided craniofacial superimposition: A survey, ACM Computing Surveys, 43, 4, pp. 1-27, (2011)
  • [3] HU Y, DUAN F, YIN B, Et al., A hierarchical dense deformable model for 3D face reconstruction from skull, Multimedia Tools & Applications, 64, 2, pp. 345-364, (2013)
  • [4] BAI F, CHENG Z, QIAO X, Et al., Face reconstruction from skull based on least squares canonical dependency analysis, IEEE International Conference on Systems, pp. 3612-3615, (2016)
  • [5] LUO L, WANG MY, TIAN Y, Et al., Automatic sex determination of skulls based on a statistical shape model, Computational & Mathematical Methods in Medicine, (2013)
  • [6] LI Y, CHANG L, QIAO X, Et al., Craniofacial reconstruction based on least square support vector regression, IEEE International Conference on Systems, pp. 1147-1151, (2014)
  • [7] FENTON T W, HEARD A N, SAUER N J., Skull-photo superimposition and border deaths: identification through exclusion and the failure to exclude, Journal of Forensic Sciences, 53, 1, pp. 34-40, (2008)
  • [8] IBANEZ O, VALSECCHI A, CAVALLI F, Et al., Study on the criteria for assessing skull-face correspondence in craniofacial superimposition, Legal Medicine, 23, pp. 59-70, (2016)
  • [9] RICCI A, MARELLA G L, APOSTOL M A., A new experimental approach to computer-aided face/skull identification in forensic anthropology, Am J Forensic Med Pathol, 27, 1, pp. 46-49, (2006)
  • [10] IBANEZ O, CORDON O, DAMAS S., A cooperative coevolutionary approach dealing with the skull-face overlay uncertainty in forensic identification by craniofacial superimposition, Soft Computing, 16, 5, pp. 797-808, (2012)
123