Implementation of artificial intelligence in chronological age estimation from orthopantomographic X-ray images of archaeological skull remains

One of the primary steps in forensic dental analysis is age estimation. Alongside sex estimation, this is offers basic categorization of subjects. Whether it is used in person-identification or archaeological analysis and research, a forensic dentist will observe these parameters when starting his work. Orthopantomographic x-ray images offer a lot of data and basically represent the golden standard for identification in forensic stomatology. Deep convolutional neural networks are establishing their presence in numerous fields of medicine and therefore we have explored the possibility of their implementation in age estimation in forensic dentistry. We developed a deep convolutional neural network, based on a dataset of 4035 orthopantomographic images, captured by and kindly provided by University of Zagreb’s, School of Dental medicine. A quick, automated and accurate model was formed that opens a new door in the field of forensic dentistry. The developed convolutional neural network was used to estimate the age of 89 archaeological skull remains. The skulls were scanned with an orthopantomography x-ray machine and the received images were used as a testing dataset. The results offered a noteworthy 73% accuracy of placing the images in correct age groups

Attention-based human age estimation from face images to enhance public security

Age estimation from facial images has gained significant attention due to its practical applications such as public security. However, one of the major challenges faced in this field is the limited availability of comprehensive training data. Moreover, due to the gradual nature of aging, similar-aged faces tend to share similarities despite their race, gender, or location. Recent studies on age estimation utilize convolutional neural networks (CNN), treating every facial region equally and disregarding potentially informative patches that contain age-specific details. Therefore, an attention module can be used to focus extra attention on important patches in the image. In this study, tests are conducted on different attention modules, namely CBAM, SENet, and Self-attention, implemented with a convolutional neural network. The focus is on developing a lightweight model that requires a low number of parameters. A merged dataset and other cutting-edge datasets are used to test the proposed model’s performance. In addition, transfer learning is used alongside the scratch CNN model to achieve optimal performance more efficiently. Experimental results on different aging face databases show the remarkable advantages of the proposed attention-based CNN model over the conventional CNN model by attaining the lowest mean absolute error and the lowest number of parameters with a better cumulative score