Kevin Bowyer

Schubmehl-Prein Family Professor

University of Notre Dame

United States

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About

Kevin Bowyer is the Schubmehl-Prein Family Professor at the University of Notre Dame in the United States. His research areas primarily focus on advancements in biometric recognition technologies, including face and iris recognition, as well as machine learning techniques for improving identification accuracy. Recent publications include work on post-mortem iris recognition, salience-based explainability in synthetic face detection, and the impact of demographic variables on face ID search systems.

Articles (17)

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2025

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Loyola University Chicago

UNITED STATES

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Kevin Bowyer

Research Interests

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Articles (17)

Beyond Mortality: Advancements in Post-Mortem Iris Recognition Through Data Collection and Computer-Aided Forensic Examination

What’s Color Got to Do With It? Face Recognition in Grayscale

Neuroevolutionary Convolutional Neural Network Design for Low-Resolution Face Recognition

Explain to Me: Salience-Based Explainability for Synthetic Face Detection Models

Analyzing the Impact of Demographic and Operational Variables on 1-to-Many Face ID Search

Improved Search in Neuroevolution Using a Neural Architecture Classifier With the CNN Architecture Encoding as Feature Vector

Iris Recognition Using an Enhanced Pre-Trained Backbone Based on Anti-Aliased CNNs

Comprehensive Study in Open-Set Iris Presentation Attack Detection

Deception Detection and Remote Physiological Monitoring: A Dataset and Baseline Experimental Results

Fair Face Verification by Using Non-Sensitive Soft-Biometric Attributes

Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology

Iris Recognition Using Low-Level CNN Layers Without Training and Single Matching

State of the Transactions on Biometrics, Behavior, and Identity Science

Fast Local Spatial Verification for Feature-Agnostic Large-Scale Image Retrieval

Robust Iris Presentation Attack Detection Fusing 2D and 3D Information

Transformation-Aware Embeddings for Image Provenance

Two-Level Genetic Algorithm for Evolving Convolutional Neural Networks for Pattern Recognition

Collaborators (5)

Aparna Bharati

Arun Ross

Patrick Flynn

Michael King

Daniel Moreira

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