no code implementations • 19 Apr 2024 • Raghavendra Ramachandra, Narayan Vetrekar, Sushma Venkatesh, Savita Nageshker, Jag Mohan Singh, R. S. Gad
In this work, we propose a novel Presentation Attack Detection (PAD) algorithm based on 3D point clouds captured using the frontal camera of a smartphone to detect presentation attacks.
no code implementations • 20 Nov 2023 • Narayan Vetrekar, Raghavendra Ramachandra, Sushma Venkatesh, Jyoti D. Pawar, R. S. Gad
We present PAD based on multispectral images constructed for eight different presentation artifacts resulted from three different artifact species.
no code implementations • 25 Oct 2023 • Raghavendra Ramachandra, Sushma Venkatesh
The proposed VeinAtnNet was trained on the newly constructed fingervein dataset with 300 unique fingervein patterns that were captured in multiple sessions to obtain 92 samples per unique fingervein.
no code implementations • 24 Sep 2023 • Raghavendra Ramachandra, Jag Mohan Singh, Sushma Venkatesh
In this paper, we present an acoustic echo-based face Presentation Attack Detection (PAD) on a smartphone in which the PAs are detected based on the reflection profiles of the transmitted signal.
no code implementations • 2 May 2023 • Raghavendra Ramachandra, Sushma Venkatesh, Guoqiang Li, Kiran Raja
Face Recognition System (FRS) are shown to be vulnerable to morphed images of newborns.
no code implementations • 7 Apr 2023 • Raghavendra Ramachandra, Sushma Venkatesh, Naser Damer, Narayan Vetrekar, Rajendra Gad
The D-MAD methods are based on using two facial images that are captured from the ePassport (also called the reference image) and the trusted device (for example, Automatic Border Control (ABC) gates) to detect whether the face image presented in ePassport is morphed.
no code implementations • 24 Mar 2023 • Raghavendra Ramachandra, Sushma Venkatesh, Gaurav Jaswal, Guoqiang Li
We present a systematic study on benchmarking the vulnerability of Face Recognition Systems (FRS) to lookalike and identical twin morphing images.
no code implementations • 24 Feb 2022 • Sankini Rancha Godage, Frøy Løvåsdal, Sushma Venkatesh, Kiran Raja, Raghavendra Ramachandra, Christoph Busch
One prevalent misconception is that an examiner's or observer's capacity for facial morph detection depends on their subject expertise, experience, and familiarity with the issue and that no works have reported the specific results of observers who regularly verify identity (ID) documents for their jobs.
no code implementations • 20 Aug 2021 • Naser Damer, Kiran Raja, Marius Süßmilch, Sushma Venkatesh, Fadi Boutros, Meiling Fang, Florian Kirchbuchner, Raghavendra Ramachandra, Arjan Kuijper
Face morphing attacks aim at creating face images that are verifiable to be the face of multiple identities, which can lead to building faulty identity links in operations like border checks.
no code implementations • 3 Nov 2020 • Sushma Venkatesh, Raghavendra Ramachandra, Kiran Raja, Christoph Busch
The vulnerability of Face Recognition System (FRS) to various kind of attacks (both direct and in-direct attacks) and face morphing attacks has received a great interest from the biometric community.
no code implementations • 3 Sep 2020 • Haoyu Zhang, Sushma Venkatesh, Raghavendra Ramachandra, Kiran Raja, Naser Damer, Christoph Busch
Extensive experiments are carried out to assess the FRS's vulnerability against the proposed morphed face generation technique on three types of data such as digital images, re-digitized (printed and scanned) images, and compressed images after re-digitization from newly generated MIPGAN Face Morph Dataset.
no code implementations • 7 Jul 2020 • Sushma Venkatesh, Haoyu Zhang, Raghavendra Ramachandra, Kiran Raja, Naser Damer, Christoph Busch
\textit{(i) Can GAN generated morphs threaten Face Recognition Systems (FRS) equally as Landmark based morphs?}
no code implementations • 6 Jul 2020 • Sushma Venkatesh, Kiran Raja, Raghavendra Ramachandra, Christoph Busch
To this extent, we have introduced a new morphed face dataset with ageing derived from the publicly available MORPH II face dataset, which we refer to as MorphAge dataset.
no code implementations • 11 Jun 2020 • Kiran Raja, Matteo Ferrara, Annalisa Franco, Luuk Spreeuwers, Illias Batskos, Florens de Wit Marta Gomez-Barrero, Ulrich Scherhag, Daniel Fischer, Sushma Venkatesh, Jag Mohan Singh, Guoqiang Li, Loïc Bergeron, Sergey Isadskiy, Raghavendra Ramachandra, Christian Rathgeb, Dinusha Frings, Uwe Seidel, Fons Knopjes, Raymond Veldhuis, Davide Maltoni, Christoph Busch
Further, we present a new online evaluation platform to test algorithms on sequestered data.
no code implementations • 15 Jan 2020 • Kiran B. Raja, R. Raghavendra, Sushma Venkatesh, Christoph Busch
To this end, we propose and extend our earlier ideas of Morton-filters for obtaining better and reliable templates for iris.
no code implementations • 5 Dec 2019 • Raghavendra Ramachandra, Martin Stokkenes, Amir Mohammadi, Sushma Venkatesh, Kiran Raja, Pankaj Wasnik, Eric Poiret, Sébastien Marcel, Christoph Busch
One of the unique features of this dataset is that it is collected in four different geographic locations representing a diverse population and ethnicity.
no code implementations • 3 Dec 2019 • Jag Mohan Singh, Sushma Venkatesh, Kiran B. Raja, Raghavendra Ramachandra, Christoph Busch
We establish the superiority of the proposed approach by benchmarking it with classical textural feature-descriptor applied directly on finger-vein images.