Sex determination from skeletons is a significant step in the analysis of forensic anthropology. Previous skeletal sex assessments were analyzed by anthropologists’ subjective vision and sexually dimorphic features. In this paper, we proposed an improved backpropagation neural network (BPNN) to determine gender from skull. It adds the momentum term to improve the convergence speed and avoids falling into local minimum. The regularization operator is used to ensure the stability of the algorithm, and the Adaboost integration algorithm is used to improve the generalization ability of the model. 267 skulls were used in the experiment, of which 153 were females and 114 were males. Six characteristics of the skull measured by computer-aided measurement are used as the network inputs. There are two structures of BPNN for experiment, namely, [6; 6; 2] and [6; 12; 2], of which the [6; 12; 2] model has better average accuracy. While η = 0.5 and α = 0.9, the classification accuracy is the best. The accuracy rate of the training stage is 97.232%, and the mean squared error (MSE) is 0.01; the accuracy rate of the testing stage is 96.764%, and the MSE is 1.016. Compared with traditional methods, it has stronger learning ability, faster convergence speed, and higher classification accuracy.

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.

Background: Ancestry estimation of skulls is under a wide range of applications in forensic science, anthropology, and facial reconstruction. This study aims to avoid defects in traditional skull ancestry estimation methods, such as time-consuming and labor-intensive manual calibration of feature points, and subjective results. Results: This paper uses the skull depth image as input, based on AlexNet, introduces the Wide module and SE-block to improve the network, designs and proposes ANINet, and realizes the ancestry classification. Such a unified model architecture of ANINet overcomes the subjectivity of manually calibrating feature points, of which the accuracy and efficiency are improved. We use depth projection to obtain the local depth image and the global depth image of the skull, take the skull depth image as the object, use global, local, and local + global methods respectively to experiment on the 95 cases of Han skull and 110 cases of Uyghur skull data sets, and perform cross-validation. The experimental results show that the accuracies of the three methods for skull ancestry estimation reached 98.21%, 98.04% and 99.03%, respectively. Compared with the classic networks AlexNet, Vgg-16, GoogLenet, ResNet-50, DenseNet-121, and SqueezeNet, the network proposed in this paper has the advantages of high accuracy and small parameters; compared with state-of-the-art methods, the method in this paper has a higher learning rate and better ability to estimate. Conclusions: In summary, skull depth images have an excellent performance in estimation, and ANINet is an effective approach for skull ancestry estimation.