RESUMEN
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.
Asunto(s)
Procesamiento de Imagen Asistido por Computador , Redes Neurales de la Computación , Cráneo/diagnóstico por imagenRESUMEN
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.
Asunto(s)
Cara , Antropología Forense/métodos , Imagenología Tridimensional/métodos , Cráneo , Adulto , Anciano , Algoritmos , Cara/anatomía & histología , Cara/diagnóstico por imagen , Femenino , Humanos , Masculino , Persona de Mediana Edad , Análisis de Regresión , Cráneo/anatomía & histología , Cráneo/diagnóstico por imagen , Máquina de Vectores de Soporte , Adulto JovenRESUMEN
Objective Aiming at the 3D skull model with great difference in initial posture and resolution, a skull registration method based on feature contour line is proposed in the paper. Methods Firstly, contour lines which include eye, nose, temporal bone, maxilla and jawbone are extracted; Secondly, the types of contour are distinguished according to their length and shortest distance between them, and the corresponding relationship is established between the two skulls needed to register, thus the coarse registration of skulls is completed; Finally, an improved iterative closest point (ICP) algorithm is proposed by integrating weight into ICP, which is used to complete fine registration of skulls, thus the accurate registration of skulls is achieved at last. Results Registration between a unknown skull and 300 reference skulls is done, and the experimental results show that the proposed registration method could complete 3D skull registration, which could get high accuracy and speed in fine registration. Conclusion So the skull registration method based on local contour line is an accurate and fast 3D skull registration method.