S
Samarth Bharadwaj
Researcher at IBM
Publications - 46
Citations - 1738
Samarth Bharadwaj is an academic researcher from IBM. The author has contributed to research in topics: Facial recognition system & Biometrics. The author has an hindex of 18, co-authored 46 publications receiving 1479 citations. Previous affiliations of Samarth Bharadwaj include Indian Institutes of Information Technology & Indraprastha Institute of Information Technology.
Papers
More filters
Proceedings ArticleDOI
Computationally Efficient Face Spoofing Detection with Motion Magnification
TL;DR: A new approach for spoofing detection in face videos using motion magnification using Eulerian motion magnification approach, which improves the state-of-art performance, especially HOOF descriptor yielding a near perfect half total error rate.
Journal ArticleDOI
Plastic Surgery: A New Dimension to Face Recognition
TL;DR: The results on the plastic surgery database suggest that it is an arduous research challenge and the current state-of-art face recognition algorithms are unable to provide acceptable levels of identification performance, so that future face recognition systems will be able to address this important problem.
Proceedings ArticleDOI
Periocular biometrics: When iris recognition fails
TL;DR: A novel algorithm to recognize periocular images in visible spectrum is proposed and the results show promise towards using peroocular region for recognition when the information is not sufficient for iris recognition.
Journal ArticleDOI
Biometric quality: a review of fingerprint, iris, and face
TL;DR: The analysis of the characteristic function of quality and match scores shows that a careful selection of complimentary set of quality metrics can provide more benefit to various applications of biometric quality.
Journal ArticleDOI
Memetically Optimized MCWLD for Matching Sketches With Digital Face Images
TL;DR: An automated algorithm to extract discriminating information from local regions of both sketches and digital face images is presented and yields better identification performance compared to existing face recognition algorithms and two commercial face recognition systems.