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20212024
most citedLog-Likelihood Score Level Fusion for Improved Cross-Sensor Smartphone Periocular Recognition

8 citations · 17 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV20243 cited

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

Xiaoning Liu, Zongwei Wu, Ao Li +109

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective netw…

cs.CV2024

E2F-Net: Eyes-to-Face Inpainting via StyleGAN Latent Space

Ahmad Hassanpour, Fatemeh Jamalbafrani, Bian Yang +3

Face inpainting, the technique of restoring missing or damaged regions in facial images, is pivotal for applications like face recognition in occluded scenarios and image analysis…

cs.CV20238 cited

Log-Likelihood Score Level Fusion for Improved Cross-Sensor Smartphone Periocular Recognition

Fernando Alonso-Fernandez, Kiran B. Raja, Christoph Busch +1

The proliferation of cameras and personal devices results in a wide variability of imaging conditions, producing large intra-class variations and a significant performance drop whe…

cs.CV2023

Differential Newborn Face Morphing Attack Detection using Wavelet Scatter Network

Raghavendra Ramachandra, Sushma Venkatesh, Guoqiang Li +1

Face Recognition System (FRS) are shown to be vulnerable to morphed images of newborns. Detecting morphing attacks stemming from face images of newborn is important to avoid unwant…

cs.CV2023

A Latent Fingerprint in the Wild Database

Xinwei Liu, Kiran Raja, Renfang Wang +9

Latent fingerprints are among the most important and widely used evidence in crime scenes, digital forensics and law enforcement worldwide. Despite the number of advancements repor…

cs.CV20232 cited

Learning Pairwise Interaction for Generalizable DeepFake Detection

Ying Xu, Kiran Raja, Luisa Verdoliva +1

A fast-paced development of DeepFake generation techniques challenge the detection schemes designed for known type DeepFakes. A reliable Deepfake detection approach must be agnosti…