Determination of Sex Discriminant Function Analysis in Chinese Human Skulls

Wen, Yang; Liu, Xiaoning; Zhu, Fei; Geng, Guohua; Li, Kang

doi:10.1007/978-3-319-97909-0_63

Cited by 3 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e accuracy rate of male discrimination was 89.4%, female discrimination was 85.0%, and the average accuracy rate of discrimination was 87.2%. Yang et al [46] used Fisher's method and logistic regression method to establish sex discriminant equation for skull with frontal bone only, and the discriminant accuracy was 67.9% and 68.7%, respectively. [45,46] and this paper use the same anatomical region, but the accuracy of this method is significantly higher than them.…”

Section: Discussionmentioning

confidence: 99%

Skull Sex Estimation Based on Wavelet Transform and Fourier Transform

Yang

Zhou

Zhang

et al. 2020

BioMed Research International

Self Cite

View full text Add to dashboard Cite

Skull sex estimation is one of the hot research topics in forensic anthropology, and has important research value in the fields of criminal investigation, archeology, anthropology, and so on. Sex estimation of skull is crucial in forensic investigations, whether in legal situations that involve living people or to identify mortal remains. The aim of this study is to establish a skull-based sex estimation model in Chinese population, providing a scientific reference for the practical application of forensic medicine and anthropology. We take the superior orbital margin and frontal bone of the skull as the research object and proposed a technology of objective sex estimation of the skull using wavelet transform and Fourier transform. Firstly, the supraorbital margin and frontal bone were quantified by wavelet transform and Fourier transform, and then the extracted features were classified by SVM, and the model was tested. The experimental results show that the accuracy rate of male and female sex discrimination is 90.9% and 94.4%, respectively, which is higher than that of morphological and measurement methods. Compared with the traditional methods, the method has more theoretical basis and objectivity, and the correct rate is higher.

show abstract

Section: Discussionmentioning

confidence: 99%

Skull Sex Estimation Based on Wavelet Transform and Fourier Transform

Yang

Zhou

Zhang

et al. 2020

BioMed Research International

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, the subject's properties for each head such as age, gender, and BMI are stored also. To eliminate the inconsistency caused by position, attitude, scale, and other factors during data acquisition, all samples are converted to the unified Frankfurt coordinate system and normalized [ 24 ], as shown in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

Craniofacial Reconstruction Method Based on Region Fusion Strategy

Wen

Zhou

Lin

et al. 2020

BioMed Research International

View full text Add to dashboard Cite

Craniofacial reconstruction is to estimate a person’s face model from the skull. It can be applied in many fields such as forensic medicine, archaeology, and face animation. Craniofacial reconstruction is based on the relationship between the skull and the face to reconstruct the facial appearance from the skull. However, the craniofacial structure is very complex and the relationship is not the same in different craniofacial regions. To better represent the shape changes of the skull and face and make better use of the correlation between different local regions, a new craniofacial reconstruction method based on region fusion strategy is proposed in this paper. This method has the flexibility of finding the nonlinear relationship between skull and face variables and is easy to solve. Firstly, the skull and face are divided into five corresponding local regions; secondly, the five regions of skull and face are mapped to low-dimensional latent space using Gaussian process latent variable model (GP-LVM), and the nonlinear features between skull and face are extracted; then, least square support vector regression (LSSVR) model is trained in latent space to establish the mapping relationship between skull region and face region; finally, perform regional fusion to achieve overall reconstruction. For the unknown skull, first divide the region, then project it into the latent space of the skull region, then use the trained LSSVR model to reconstruct the face of the corresponding region, and finally perform regional fusion to realize the face reconstruction of the unknown skull. The experimental results show that the method is effective. Compared with other regression methods, our method is optimal. In addition, we add attributes such as age and body mass index (BMI) to the mappings to achieve face reconstruction with different attributes.

show abstract

“…In addition, the skull needs coordinate correction and scale normalization. In the coordinate correction, the Frankfurt coordinate system [14] is used to unify the coordinates of all skulls. In scale normalization, the distance from the middle point of the left ear hole to the middle point of the right ear hole, regardless of the size of the skull, is treated as unit 1, that is to say, each vertex of the skull (x, y, z) is converted to (x/|L p − R p |, y/|L p − R p |, z/|L p − R p |).…”

Section: Acquisition and Preprocessing Of Skull Datamentioning

confidence: 99%

A Hierarchical Skull Point Cloud Registration Method

et al. 2019

View full text Add to dashboard Cite

Skull registration is one of the important steps in craniofacial reconstruction, and its registration accuracy and efficiency have an important impact on the reconstruction results. To solve the problem of low accuracy and efficiency of existing skull registration methods, a hierarchical skull point cloud registration method is proposed in this paper. The whole registration process is divided into a rough registration stage and a fine registration stage. Firstly, feature points are extracted from the pre-processed skull point cloud model, and a local coordinate reference system is established according to the feature points and their neighbor points. The improved spin image is used to construct the local feature descriptor. The feature matching is carried out according to the nearest neighbor algorithm, and the k-means algorithm is used to eliminate the mismatching points to achieve skull rough registration. Then, based on rough registration, we use an improved ICP algorithm to achieve fine registration of the skull. In this process, we use random sampling to reduce the search scale of points and add geometric feature constraints to further eliminate mismatched points. Finally, the whole registration algorithm is applied to the skull point cloud data to verify. The experimental results show that, compared with other methods, the registration effect and efficiency of the proposed method are superior to those of other methods. In order to verify the universality of the method, we also use a common data set for verification. Experiments show that the method is also very effective.

show abstract

Determination of Sex Discriminant Function Analysis in Chinese Human Skulls

Cited by 3 publications

References 12 publications

Skull Sex Estimation Based on Wavelet Transform and Fourier Transform

Skull Sex Estimation Based on Wavelet Transform and Fourier Transform

Craniofacial Reconstruction Method Based on Region Fusion Strategy

A Hierarchical Skull Point Cloud Registration Method

Contact Info

Product

Resources

About