ANINet: a deep neural network for skull ancestry estimation

Lin Pengyue,Geng Guohua,Xia Siyuan,Yang Wen,Liu Xiaoning,Wang Shixiong,Jiang Yi

doi:10.1186/s12859-021-04444-6

Abstract

BackgroundAncestry 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.ResultsThis 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.ConclusionsIn summary, skull depth images have an excellent performance in estimation, and ANINet is an effective approach for skull ancestry estimation.

Highlights

In the field of forensics, when identifying unknown corpses, forensic experts generally rely on experience to directly determine or use the sex chromatin in the tissue cells of the corpse or hormones in the blood to infer the sex, age, ancestry and other information of the deceased
This paper proposed two skull projection methods, combined with ANINet to realize the automatic estimation of skull ancestors
We propose ANINet, a new neural network for skull ancestry estimation, which has high recognition accuracy and few parameters

Summary

Introduction

In the field of forensics, when identifying unknown corpses, forensic experts generally rely on experience to directly determine or use the sex chromatin in the tissue cells of the corpse or hormones in the blood to infer the sex, age, ancestry and other information of the deceased. Some morphological characteristics of the skull are used to assess ancestry, such as cranial index [10], bone shape [11,12,13], facial protrusion [14], nasal bone shape [15], etc. When evaluating these unmeasurable characteristics, the interaction between gender and ancestry must be taken into account. The determination of the unknown skull ancestry is the first and important step in the identification of forensic anthropology. 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

Objectives

Methods

Results