Abstract
Although viable tools are available for the identification of unknown deceased individuals, recognition rates with these methods are greatly impacted by the degree to which decomposition has occurred. Therefore, identifying highly decomposed remains poses a major challenge. This paper analyzes the effect of facial decomposition on the recognition rates of several facial recognition commercial-off-theshelf systems and research-grade systems, as well as algorithms contained in a custom recognition library. The custom dataset of facial images used in the experiment is composed of 42 subjects at stages of decomposition rangingfrom recently deceased to later stages where the soft tissues are severely decomposed and facial features are deformed. It is shown that an algorithm’s ability to correctly detect a decomposing face is a crucial first step that not all face models can accurately handle. However, some of the evaluated Convolution Neural Network (CNN)–inspired methods provide promising results even in cases of severely decomposed faces.
