Forensic Iris Image-Based Post-Mortem Interval Estimation

• URL: http://arxiv.org/abs/2404.10172v2
• Date: Sun, 28 Apr 2024 20:15:25 GMT
• Title: Forensic Iris Image-Based Post-Mortem Interval Estimation
• Authors: Rasel Ahmed Bhuiyan, Adam Czajka,
• Abstract summary: Post-mortem interval (PMI) is correlated with the number of hours that have elapsed since death. This paper presents the first known to us method of PMI estimation directly from forensic iris images.
• Score: 4.737519767218666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• Forensic Iris Image Synthesis [5.596752018167751]
  Post-mortem iris recognition is an emerging application of iris-based human identification in a forensic setup. This paper offers a conditional StyleGAN-based iris synthesis model, trained on the largest-available dataset of post-mortem iris samples.
  arXiv  Detail & Related papers  (2023-12-07T08:28:41Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Evaluation of importance estimators in deep learning classifiers for Computed Tomography [1.6710577107094642]
  Interpretability of deep neural networks often relies on estimating the importance of input features. Two versions of SmoothGrad topped the fidelity and ROC rankings, whereas both Integrated Gradients and SmoothGrad excelled in DSC evaluation. There was a critical discrepancy between model-centric (fidelity) and human-centric (ROC and DSC) evaluation.
  arXiv  Detail & Related papers  (2022-09-30T11:57:25Z)
• Forensicability Assessment of Questioned Images in Recapturing Detection [78.45849869266834]
  We propose a forensicability assessment network to quantify the forensicability of the questioned samples. The low-forensicability samples are rejected before the actual recapturing detection process to improve the efficiency of recapturing systems. We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing and recaptured document detection tasks.
  arXiv  Detail & Related papers  (2022-09-05T12:26:01Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• Deep regression for uncertainty-aware and interpretable analysis of large-scale body MRI [1.6799377888527687]
  Large-scale medical studies such as the UK Biobank examine thousands of volunteer participants with medical imaging techniques. Recent approaches with convolutional neural networks for regression can perform these evaluations automatically. On magnetic resonance imaging (MRI) data of more than 40,000 UK Biobank subjects, these systems can estimate human age, body composition and more.
  arXiv  Detail & Related papers  (2021-05-17T13:12:20Z)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)
• Assignment Flow for Order-Constrained OCT Segmentation [0.0]
  The identification of retinal layer thicknesses serves as an essential task be done for each patient separately. The elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven textitgeometric approach to order-constrained 3D OCT retinal cell layer segmentation
  arXiv  Detail & Related papers  (2020-09-10T01:57:53Z)
• Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty [32.33791302617957]
  We introduce segmentation networks (SSNs), an efficient probabilistic method for modelling aleatoric uncertainty with any image segmentation network architecture. SSNs can generate multiple spatially coherent hypotheses for a single image. We tested our method on the segmentation of real-world medical data, including lung nodules in 2D CT and brain tumours in 3D multimodal MRI scans.
  arXiv  Detail & Related papers  (2020-06-10T18:06:41Z)
• 1-D Convlutional Neural Networks for the Analysis of Pupil Size Variations in Scotopic Conditions [79.71065005161566]
  1-D convolutional neural network models are trained for classification of short-range sequences. Model provides prediction with high average accuracy on a hold out test set.
  arXiv  Detail & Related papers  (2020-02-06T17:25:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.