173 citations · 293 across the 19 of their papers we have counts for
24 papers
Haven't I Seen You Before? Assessing Identity Leakage in Synthetic Irises
Patrick Tinsley, Adam Czajka, Patrick Flynn
Generative Adversarial Networks (GANs) have proven to be a preferred method of synthesizing fake images of objects, such as faces, animals, and automobiles. It is not surprising th…
Analyzing the Impact of Shape & Context on the Face Recognition Performance of Deep Networks
Sandipan Banerjee, Walter Scheirer, Kevin Bowyer +1
In this article, we analyze how changing the underlying 3D shape of the base identity in face images can distort their overall appearance, especially from the perspective of deep f…
Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition
Andrey Kuehlkamp, Aidan Boyd, Adam Czajka +4
Iris recognition of living individuals is a mature biometric modality that has been adopted globally from governmental ID programs, border crossing, voter registration and de-dupli…
Digital and Physical-World Attacks on Remote Pulse Detection
Jeremy Speth, Nathan Vance, Patrick Flynn +2
Remote photoplethysmography (rPPG) is a technique for estimating blood volume changes from reflected light without the need for a contact sensor. We present the first examples of p…
Deception Detection and Remote Physiological Monitoring: A Dataset and Baseline Experimental Results
Jeremy Speth, Nathan Vance, Adam Czajka +3
We present the Deception Detection and Physiological Monitoring (DDPM) dataset and initial baseline results on this dataset. Our application context is an interview scenario in whi…
Remote Pulse Estimation in the Presence of Face Masks
Jeremy Speth, Nathan Vance, Patrick Flynn +2
Remote photoplethysmography (rPPG), a family of techniques for monitoring blood volume changes, may be especially useful for widespread contactless health monitoring using face vid…
This Face Does Not Exist ... But It Might Be Yours! Identity Leakage in Generative Models
Patrick Tinsley, Adam Czajka, Patrick Flynn
Generative adversarial networks (GANs) are able to generate high resolution photo-realistic images of objects that "do not exist." These synthetic images are rather difficult to de…
Learning Transformation-Aware Embeddings for Image Forensics
Aparna Bharati, Daniel Moreira, Patrick Flynn +3
A dramatic rise in the flow of manipulated image content on the Internet has led to an aggressive response from the media forensics research community. New efforts have incorporate…
Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval
Joel Brogan, Aparna Bharati, Daniel Moreira +4
Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working onContent-Ba…
Fast Face Image Synthesis with Minimal Training
Sandipan Banerjee, Walter J. Scheirer, Kevin W. Bowyer +1
We propose an algorithm to generate realistic face images of both real and synthetic identities (people who do not exist) with different facial yaw, shape and resolution.The synthe…
On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs
Sandipan Banerjee, Walter J. Scheirer, Kevin W. Bowyer +1
We propose a multi-scale GAN model to hallucinate realistic context (forehead, hair, neck, clothes) and background pixels automatically from a single input face mask. Instead of sw…
Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition
Adam Czajka, Daniel Moreira, Kevin W. Bowyer +1
Binarized statistical image features (BSIF) have been successfully used for texture analysis in many computer vision tasks, including iris recognition and biometric presentation at…
Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-driven Verification
Daniel Moreira, Mateusz Trokielewicz, Adam Czajka +2
This paper advances the state of the art in human examination of iris images by (1) assessing the impact of different iris conditions in identity verification, and (2) introducing…
Beyond Pixels: Image Provenance Analysis Leveraging Metadata
Aparna Bharati, Daniel Moreira, Joel Brogan +5
Creative works, whether paintings or memes, follow unique journeys that result in their final form. Understanding these journeys, a process known as "provenance analysis", provides…
On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques
Pei Li, Loreto Prieto, Domingo Mery +1
Although face recognition systems have achieved impressive performance in recent years, the low-resolution face recognition (LRFR) task remains challenging, especially when the LR…
Face Recognition in Low Quality Images: A Survey
Pei Li, Loreto Prieto, Domingo Mery +1
Low-resolution face recognition (LRFR) has received increasing attention over the past few years. Its applications lie widely in the real-world environment when high-resolution or…
Image Provenance Analysis at Scale
Daniel Moreira, Aparna Bharati, Joel Brogan +6
Prior art has shown it is possible to estimate, through image processing and computer vision techniques, the types and parameters of transformations that have been applied to the c…
Provenance Filtering for Multimedia Phylogeny
Allan Pinto, Daniel Moreira, Aparna Bharati +5
Departing from traditional digital forensics modeling, which seeks to analyze single objects in isolation, multimedia phylogeny analyzes the evolutionary processes that influence d…
U-Phylogeny: Undirected Provenance Graph Construction in the Wild
Aparna Bharati, Daniel Moreira, Allan Pinto +5
Deriving relationships between images and tracing back their history of modifications are at the core of Multimedia Phylogeny solutions, which aim to combat misinformation through…
Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization
Joel Brogan, Paolo Bestagini, Aparna Bharati +6
As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows. In this paper, w…
SREFI: Synthesis of Realistic Example Face Images
Sandipan Banerjee, John S. Bernhard, Walter J. Scheirer +2
In this paper, we propose a novel face synthesis approach that can generate an arbitrarily large number of synthetic images of both real and synthetic identities. Thus a face image…
To Frontalize or Not To Frontalize: Do We Really Need Elaborate Pre-processing To Improve Face Recognition?
Sandipan Banerjee, Joel Brogan, Janez Krizaj +5
Face recognition performance has improved remarkably in the last decade. Much of this success can be attributed to the development of deep learning techniques such as convolutional…
The ND-IRIS-0405 Iris Image Dataset
Kevin W. Bowyer, Patrick J. Flynn
The Computer Vision Research Lab at the University of Notre Dame began collecting iris images in the spring semester of 2004. The initial data collections used an LG 2200 iris imag…
Hierarchical Clustering in Face Similarity Score Space
Jason Grant, Patrick Flynn
Similarity scores in face recognition represent the proximity between pairs of images as computed by a matching algorithm. Given a large set of images and the proximities between a…