R
Raghavendra Ramachandra
Researcher at Norwegian University of Science and Technology
Publications - 17
Citations - 134
Raghavendra Ramachandra is an academic researcher from Norwegian University of Science and Technology. The author has contributed to research in topics: Facial recognition system & Deep learning. The author has an hindex of 4, co-authored 17 publications receiving 42 citations.
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Proceedings ArticleDOI
Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report
Andrey Ignatov,Cheng-Ming Chiang,Hsien-Kai Kuo,Anastasia Sycheva,Radu Timofte,Min-Hung Chen,Man-Yu Lee,Yu-Syuan Xu,Yu Tseng,Shusong Xu,Jin Guo,Chao-Hung Chen,Ming-Chun Hsyu,Wen-Chia Tsai,Chao-Wei Chen,Grigory Malivenko,Minsu Kwon,Myungje Lee,Jaeyoon Yoo,Changbeom Kang,Shinjo Wang,Zheng Shaolong,Hao Dejun,Xie Fen,Feng Zhuang,Yipeng Ma,Jingyang Peng,Tao Wang,Fenglong Song,Chih-Chung Hsu,Kwan-Lin Chen,Mei-Hsuang Wu,Vishal Chudasama,Kalpesh Prajapati,Heena Patel,Anjali Sarvaiya,Kishor P. Upla,Kiran B. Raja,Raghavendra Ramachandra,Christoph Busch,Etienne de Stoutz +40 more
TL;DR: In this article, an end-to-end deep learning-based image signal processing (ISP) pipeline that can replace classical hand-crafted ISPs and achieve nearly real-time performance on smartphone NPUs was developed.
Journal ArticleDOI
Adversarial Attacks Against Face Recognition: A Comprehensive Study
TL;DR: A comprehensive survey on adversarial attacks against face recognition systems is presented in this paper, where a taxonomy of existing attack and defense methods based on different criteria is proposed, and the challenges and potential research direction are discussed.
Proceedings ArticleDOI
On the Applicability of Synthetic Data for Face Recognition
TL;DR: In this article, the suitability of synthetic face images generated with StyleGAN and StyleGAN2 to compensate for the urgent lack of publicly available largescale test data was investigated for the European Entry/Exit System, which integrates face recognition mechanisms.
Posted Content
On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detection
TL;DR: A systematic investigation on the vulnerability of the Commercial-Off- The-Shelf (COTS) FRS when morphed images under the influence of ageing are presented and a new evaluation metric, namely the Fully Mated Morphed Presentation Match Rate (FMMPMR), is introduced to quantify the vulnerability effectively in a realistic scenario.
Proceedings ArticleDOI
On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detection
TL;DR: In this paper, the authors report a systematic investigation on the vulnerability of commercial-off-the-shelf (COTS) FRS when morphed images under the influence of ageing are presented and introduce a new morphed face dataset with ageing derived from the publicly available MORPH II face dataset, which they refer to as MorphAge dataset.