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20172022
most citedCTC-Segmentation of Large Corpora for German End-to-end Speech Recognition

79 citations · 144 across the 9 of their papers we have counts for

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Showing cs.CVShow all

18 papers · 1 filter

cs.CV2022

Face Morphing: Fooling a Face Recognition System Is Simple!

Stefan Hörmann, Tianlin Kong, Torben Teepe +3

State-of-the-art face recognition (FR) approaches have shown remarkable results in predicting whether two faces belong to the same identity, yielding accuracies between 92% and 100…

cs.CV20228 cited

Towards a Deeper Understanding of Skeleton-based Gait Recognition

Torben Teepe, Johannes Gilg, Fabian Herzog +2

Gait recognition is a promising biometric with unique properties for identifying individuals from a long distance by their walking patterns. In recent years, most gait recognition…

cs.CV2021

Attention-based Partial Face Recognition

Stefan Hörmann, Zeyuan Zhang, Martin Knoche +2

Photos of faces captured in unconstrained environments, such as large crowds, still constitute challenges for current face recognition approaches as often faces are occluded by obj…

cs.CV2021

How to Design a Three-Stage Architecture for Audio-Visual Active Speaker Detection in the Wild

Okan Köpüklü, Maja Taseska, Gerhard Rigoll

Successful active speaker detection requires a three-stage pipeline: (i) audio-visual encoding for all speakers in the clip, (ii) inter-speaker relation modeling between a referenc…

cs.CV2021

GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition

Torben Teepe, Ali Khan, Johannes Gilg +3

Gait recognition is a promising video-based biometric for identifying individual walking patterns from a long distance. At present, most gait recognition methods use silhouette ima…

cs.CV2020

Driver Anomaly Detection: A Dataset and Contrastive Learning Approach

Okan Köpüklü, Jiapeng Zheng, Hang Xu +1

Distracted drivers are more likely to fail to anticipate hazards, which result in car accidents. Therefore, detecting anomalies in drivers' actions (i.e., any action deviating from…