Sex Determination of Incomplete Skull of Han Ethnic in China

Liu, Xiaoning; Liu, Xiongle; Zhu, Lipin; Zhao, Qianna; Geng, Guohua

doi:10.1007/978-3-319-63315-2_50

Cited by 2 publications

(6 citation statements)

References 9 publications

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“…From Table 2 , it can be seen that when selecting a group of suitable parameter values ( s =2, β =0.5, p 1 =0.9, p 2 =0.1, σ 1 =30, and σ 2 =110), the MKDSIF-FCM algorithm can obtain the best classification accuracy of sex determination of the skull. For 186 skulls of the Han Chinese ethnic group, we obtain the accuracy of 95.70% compared to 87.09%, 92.2%, and 93.55% found in the literature [ 15 – 17 ], respectively. There is a classification accuracy of 93.02% for males and 98% for females, respectively.…”

Section: Resultsmentioning

confidence: 59%

“…In particular, logistic regression and discriminant function analysis are the two most representative statistical learning methods. According to the method used in literature [ 17 ], we established the best model using logistic regression and stepwise variable selection. When selecting nine variables (I8, I11, I14, I16, I20, I29, I31, I38, I40), the model obtains 84.93% and 92.53% classification rates for males and females, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…The experimental results suggest that the use of the proposed MKDSIF-FCM algorithm in the classification of the skull dataset is an accurate, robust, and reproducible technique. For the Han Chinese ethnic group, there is an accuracy improvement of nearly 8.6%, 3.5%, and 2.2% for our sex determination approach over other methods in the literature [ 15 – 17 ].…”

Section: Discussionmentioning

confidence: 99%

“…Then, they used the forward stepwise regression method based on maximum likelihood estimation to select the optimal feature subset of each partition. Experiments showed that any three partitions are enough to determine the sex of incomplete skulls with a high accuracy [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Sex Determination of 3D Skull Based on a Novel Unsupervised Learning Method

Gao

Geng

Wen

2018

Computational and Mathematical Methods in Medicine

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In law enforcement investigation cases, sex determination from skull morphology is one of the important steps in establishing the identity of an individual from unidentified human skeleton. To our knowledge, existing studies of sex determination of the skull mostly utilize supervised learning methods to analyze and classify data and can have limitations when applied to actual cases with the absence of category labels in the skull samples or a large difference in the number of male and female samples of the skull. This paper proposes a novel approach which is based on an unsupervised classification technique in performing sex determination of the skull of Han Chinese ethnic group. The 78 landmarks on the outer surface of 3D skull models from computed tomography scans are marked, and a skull dataset of a total of 40 interlandmark measurements is constructed. A stable and efficient unsupervised algorithm which we abbreviated as MKDSIF-FCM is proposed to address the classification problem for the skull dataset. The experimental results of the adult skull suggest that the proposed MKDSIF-FCM algorithm warrants fairly high sex determination accuracy for females and males, which is 98.0% and 93.02%, respectively, and is superior to all the classification methods we attempted. As a result of its fairly high accuracy, extremely good stability, and the advantage of unsupervised learning, the proposed method is potentially applicable for forensic investigations and archaeological studies.

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Section: Resultsmentioning

confidence: 59%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sex Determination of 3D Skull Based on a Novel Unsupervised Learning Method

Gao

Geng

Wen

2018

Computational and Mathematical Methods in Medicine

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“…erefore, the correct rate is also higher. Liu et al [45] took the frontal bone as the experimental object and used the forward stepwise regression method based on the maximum likelihood estimation to establish the frontal bone sex discrimination model. e accuracy rate of male discrimination was 89.4%, female discrimination was 85.0%, and the average accuracy rate of discrimination was 87.2%.…”

Section: Discussionmentioning

confidence: 99%

Skull Sex Estimation Based on Wavelet Transform and Fourier Transform

Yang

Zhou

Zhang

et al. 2020

BioMed Research International

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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.

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Sex Determination of Incomplete Skull of Han Ethnic in China

Cited by 2 publications

References 9 publications

Sex Determination of 3D Skull Based on a Novel Unsupervised Learning Method

Sex Determination of 3D Skull Based on a Novel Unsupervised Learning Method

Skull Sex Estimation Based on Wavelet Transform and Fourier Transform

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