output
20022026
most citedPost-mortem Human Iris Recognition

30 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing cs.CVShow all

18 papers · 1 filter

cs.CV2024★ 5 cited

Improving Object Detection Quality in Football Through Super-Resolution Techniques

Karolina Seweryn, Gabriel Chęć, Szymon Łukasik +1

This study explores the potential of super-resolution techniques in enhancing object detection accuracy in football. Given the sport's fast-paced nature and the critical importance…

cs.CV2023

Survey of Action Recognition, Spotting and Spatio-Temporal Localization in Soccer -- Current Trends and Research Perspectives

Karolina Seweryn, Anna Wróblewska, Szymon Łukasik

Action scene understanding in soccer is a challenging task due to the complex and dynamic nature of the game, as well as the interactions between players. This article provides a c…

cs.CV2023★ 28 cited

SoccerNet 2023 Challenges Results

Anthony Cioppa, Silvio Giancola, Vladimir Somers +99

The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vi…

cs.CV2018★ 1 cited

Thermal Features for Presentation Attack Detection in Hand Biometrics

Ewelina Bartuzi, Mateusz Trokielewicz

This paper proposes a method for utilizing thermal features of the hand for the purpose of presentation attack detection (PAD) that can be employed in a hand biometrics system's pi…

cs.CV2018

Iris recognition in cases of eye pathology

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz

This chapter provides insight on how iris recognition, one of the leading biometric identification technologies in the world, can be impacted by pathologies and illnesses present i…

cs.CV2018★ 22 cited

Iris Recognition with a Database of Iris Images Obtained in Visible Light Using Smartphone Camera

Mateusz Trokielewicz

This paper delivers a new database of iris images collected in visible light using a mobile phone's camera and presents results of experiments involving existing commercial and ope…

cs.CV2018★ 15 cited

Iris and periocular recognition in arabian race horses using deep convolutional neural networks

Mateusz Trokielewicz, Mateusz Szadkowski

This paper presents a study devoted to recognizing horses by means of their iris and periocular features using deep convolutional neural networks (DCNNs). Identification of race ho…

cs.CV2018★ 11 cited

Database of iris images acquired in the presence of ocular pathologies and assessment of iris recognition reliability for disease-affected eyes

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz

This paper presents a database of iris images collected from disease affected eyes and an analysis related to the influence of ocular diseases on iris recognition reliability. For…

cs.CV2018★ 9 cited

Cataract influence on iris recognition performance

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz

This paper presents the experimental study revealing weaker performance of the automatic iris recognition methods for cataract-affected eyes when compared to healthy eyes. There is…

cs.CV2018★ 30 cited

Post-mortem Human Iris Recognition

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz

This paper presents a unique analysis of post-mortem human iris recognition. Post-mortem human iris images were collected at the university mortuary in three sessions separated by…

cs.CV2018★ 24 cited

Assessment of iris recognition reliability for eyes affected by ocular pathologies

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz

This paper presents an analysis of how the iris recognition is impacted by eye diseases and an appropriate dataset comprising 2996 iris images of 230 distinct eyes (including 184 i…

cs.CV2018★ 4 cited

Iris Recognition Under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz

This paper presents the most comprehensive analysis of iris recognition reliability in the occurrence of various biological processes happening naturally and pathologically in the…

cs.CV2018★ 25 cited

Human Iris Recognition in Post-mortem Subjects: Study and Database

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz

This paper presents a unique study of post-mortem human iris recognition and the first known to us database of near-infrared and visible-light iris images of deceased humans collec…

cs.CV2018★ 13 cited

Linear regression analysis of template aging in iris biometrics

Mateusz Trokielewicz

The aim of this work is to determine how vulnerable different iris coding methods are in relation to biometric template aging phenomenon. This is considered to be particularly impo…

cs.CV2018★ 21 cited

Implications of Ocular Pathologies for Iris Recognition Reliability

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz

This paper presents an analysis of how iris recognition is influenced by eye disease and an appropriate dataset comprising 2996 images of irises taken from 230 distinct eyes (inclu…

cs.CV2018★ 2 cited

MobiBits: Multimodal Mobile Biometric Database

Ewelina Bartuzi, Katarzyna Roszczewska, Mateusz Trokielewicz +1

This paper presents a novel database comprising representations of five different biometric characteristics, collected in a mobile, unconstrained or semi-constrained setting with t…

cs.CV2018★ 17 cited

Data-Driven Segmentation of Post-mortem Iris Images

Mateusz Trokielewicz, Adam Czajka

This paper presents a method for segmenting iris images obtained from the deceased subjects, by training a deep convolutional neural network (DCNN) designed for the purpose of sema…

cs.CV2018★ 4 cited

Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art

Adam Czajka, Kevin W. Bowyer

Iris recognition is increasingly used in large-scale applications. As a result, presentation attack detection for iris recognition takes on fundamental importance. This survey cove…