Forensic Iris Image-Based Post-Mortem Interval Estimation
Rasel Ahmed Bhuiyan Adam Czajka
Post-mortem iris recognition is an emerging application of iris-based human
identification in a forensic setup. One factor that may be useful in
conditioning iris recognition methods is the tissue decomposition level, which
is correlated with the post-mortem interval (PMI), i.g., the number of hours
that have elapsed since death. PMI, however, is not always available, and its
precise estimation remains one of the core challenges in forensic examination.
This paper presents the first known to us method of PMI estimation directly
from forensic iris images. To assess the feasibility of the iris-based PMI
estimation, convolutional neural networks-based models (VGG19, DenseNet121,
ResNet152, and Inception_v3) were trained to predict the PMI from (a)
near-infrared (NIR), (b) visible (RGB), and (c) multispectral forensic iris
images. Models were evaluated following a 10-fold cross-validation in (S1)
sample-disjoint, (S2) subject-disjoint, and (S3) cross-dataset scenarios. We
found that using the multispectral data offers a spectacularly low mean
absolute error (MAE) of approximately 3.5 hours in scenario (S1), a bit worse
MAE of approximately 17.5 hours in scenario (S2), and an MAE of approximately
69.0 hours of in the scenario (S3). This suggests that if the environmental
conditions are favorable (e.g., bodies are kept in low temperatures), forensic
iris images provide features that are indicative of the PMI and can be
automatically estimated. The source codes and model weights are made available
with the paper.