Search Results for author: Raghavendra Ramachandra

Found 32 papers, 4 papers with code

Vulnerability of Face Morphing Attacks: A Case Study on Lookalike and Identical Twins

no code implementations24 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.

Benchmarking Face Recognition

Finger-NestNet: Interpretable Fingerphoto Verification on Smartphone using Deep Nested Residual Network

no code implementations9 Dec 2022 Raghavendra Ramachandra, Hailin Li

Fingerphoto images captured using a smartphone are successfully used to verify the individuals that have enabled several applications.

Deep Composite Face Image Attacks: Generation, Vulnerability and Detection

no code implementations20 Nov 2022 Jag Mohan Singh, Raghavendra Ramachandra

Given the face images corresponding to two unique data subjects, the proposed CFIA method will independently generate the segmented facial attributes, then blend them using transparent masks to generate the CFIA samples.

Face Recognition

Reliable Face Morphing Attack Detection in On-The-Fly Border Control Scenario with Variation in Image Resolution and Capture Distance

no code implementations30 Sep 2022 Jag Mohan Singh, Raghavendra Ramachandra

Among these attacks, face morphing attacks are highly potential in deceiving automatic FRS and human observers and indicate a severe security threat, especially in the border control scenario.

Face Recognition

A Uniform Representation Learning Method for OCT-based Fingerprint Presentation Attack Detection and Reconstruction

no code implementations25 Sep 2022 Wentian Zhang, Haozhe Liu, Feng Liu, Raghavendra Ramachandra

For reconstruction performance, our method achieves the best performance with 0. 834 mIOU and 0. 937 PA. By comparing with the recognition performance on surface 2D fingerprints, the effectiveness of our proposed method on high quality subsurface fingerprint reconstruction is further proved.

Representation Learning Semantic Segmentation

How Far Can I Go ? : A Self-Supervised Approach for Deterministic Video Depth Forecasting

1 code implementation1 Jul 2022 Sauradip Nag, Nisarg Shah, Anran Qi, Raghavendra Ramachandra

Unlike previous methods, we model the depth estimation of the unobserved frame as a view-synthesis problem, which treats the depth estimate of the unseen video frame as an auxiliary task while synthesizing back the views using learned pose.

Depth Estimation Pose Estimation +1

Analyzing Human Observer Ability in Morphing Attack Detection -- Where Do We Stand?

no code implementations24 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.

3D Face Morphing Attacks: Generation, Vulnerability and Detection

no code implementations10 Jan 2022 Jag Mohan Singh, Raghavendra Ramachandra

To this extent, we have introduced a novel approach based on blending the 3D face point clouds corresponding to the contributory data subjects.

Face Recognition

Generation of Non-Deterministic Synthetic Face Datasets Guided by Identity Priors

no code implementations7 Dec 2021 Marcel Grimmer, Haoyu Zhang, Raghavendra Ramachandra, Kiran Raja, Christoph Busch

Mated samples are generated by manipulating latent vectors, and more precisely, we exploit Principal Component Analysis (PCA) to define semantically meaningful directions in the latent space and control the similarity between the original and the mated samples using a pre-trained face recognition system.

Face Image Quality Face Recognition

Algorithmic Fairness in Face Morphing Attack Detection

no code implementations23 Nov 2021 Raghavendra Ramachandra, Kiran Raja, Christoph Busch

In this paper, we study and present a comprehensive analysis of algorithmic fairness of the existing Single image-based Morph Attack Detection (S-MAD) algorithms.

Face Recognition Fairness

FRT-PAD: Effective Presentation Attack Detection Driven by Face Related Task

no code implementations22 Nov 2021 Wentian Zhang, Haozhe Liu, Feng Liu, Raghavendra Ramachandra, Christoph Busch

The proposed method, first introduces task specific features from other face related task, then, we design a Cross-Modal Adapter using a Graph Attention Network (GAT) to re-map such features to adapt to PAD task.

Face Presentation Attack Detection Face Recognition +1

DFCANet: Dense Feature Calibration-Attention Guided Network for Cross Domain Iris Presentation Attack Detection

no code implementations1 Nov 2021 Gaurav Jaswal, Aman Verma, Sumantra Dutta Roy, Raghavendra Ramachandra

To alleviate these shortcomings, this paper proposes DFCANet: Dense Feature Calibration and Attention Guided Network which calibrates the locally spread iris patterns with the globally located ones.

Cross-Domain Iris Presentation Attack Detection Incremental Learning +1

Taming Self-Supervised Learning for Presentation Attack Detection: In-Image De-Folding and Out-of-Image De-Mixing

1 code implementation9 Sep 2021 Zhe Kong, Wentian Zhang, Feng Liu, Wenhan Luo, Haozhe Liu, Linlin Shen, Raghavendra Ramachandra

Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem.

Self-Supervised Learning

ReGenMorph: Visibly Realistic GAN Generated Face Morphing Attacks by Attack Re-generation

no code implementations20 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.

Face Recognition

On the Applicability of Synthetic Data for Face Recognition

no code implementations6 Apr 2021 Haoyu Zhang, Marcel Grimmer, Raghavendra Ramachandra, Kiran Raja, Christoph Busch

Face verification has come into increasing focus in various applications including the European Entry/Exit System, which integrates face recognition mechanisms.

Face Image Quality Face Image Quality Assessment +2

Face Morphing Attack Generation & Detection: A Comprehensive Survey

no code implementations3 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.

Benchmarking Face Recognition

On Benchmarking Iris Recognition within a Head-mounted Display for AR/VR Application

no code implementations20 Oct 2020 Fadi Boutros, Naser Damer, Kiran Raja, Raghavendra Ramachandra, Florian Kirchbuchner, Arjan Kuijper

Motivated by the performance of iris recognition, we also propose the continuous authentication of users in a non-collaborative capture setting in HMD.

Benchmarking Iris Recognition

MIPGAN -- Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN

no code implementations3 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.

Face Generation Face Recognition

Adversarial Attacks against Face Recognition: A Comprehensive Study

no code implementations22 Jul 2020 Fatemeh Vakhshiteh, Ahmad Nickabadi, Raghavendra Ramachandra

Face recognition (FR) systems have demonstrated outstanding verification performance, suggesting suitability for real-world applications ranging from photo tagging in social media to automated border control (ABC).

Face Recognition

On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detection

no code implementations6 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.

Face Recognition

A Survey on Unknown Presentation Attack Detection for Fingerprint

no code implementations17 May 2020 Jag Mohan Singh, Ahmed Madhun, Guoqiang Li, Raghavendra Ramachandra

Fingerprint recognition systems are widely deployed in various real-life applications as they have achieved high accuracy.

Smartphone Multi-modal Biometric Authentication: Database and Evaluation

no code implementations5 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.

Robust Morph-Detection at Automated Border Control Gate using Deep Decomposed 3D Shape and Diffuse Reflectance

no code implementations3 Dec 2019 Jag Mohan Singh, Raghavendra Ramachandra, Kiran B. Raja, Christoph Busch

Face recognition is widely employed in Automated Border Control (ABC) gates, which verify the face image on passport or electronic Machine Readable Travel Document (eMTRD) against the captured image to confirm the identity of the passport holder.

Face Recognition

Detecting Finger-Vein Presentation Attacks Using 3D Shape & Diffuse Reflectance Decomposition

no code implementations3 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.

Benchmarking Finger Vein Recognition

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