F
Fayin Li
Researcher at George Mason University
Publications - 21
Citations - 2690
Fayin Li is an academic researcher from George Mason University. The author has contributed to research in topics: Facial recognition system & Boosting (machine learning). The author has an hindex of 12, co-authored 21 publications receiving 2353 citations.
Papers
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Journal ArticleDOI
Micro-Doppler effect in radar: phenomenon, model, and simulation study
TL;DR: In this paper, the micro-Doppler effect was introduced in radar data, and a model of Doppler modulations was developed to derive formulas of micro-doppler induced by targets with vibration, rotation, tumbling and coning motions.
Journal ArticleDOI
Analysis of micro-Doppler signatures
TL;DR: In this article, the authors introduced the micro-Doppler effect in radar and developed the mathematics of micro-doppler signatures, which enable some properties of the target to be determined.
Journal ArticleDOI
Integrating perceptual and cognitive modeling for adaptive and intelligent human-computer interaction
Zoran Duric,Wayne D. Gray,R. Heishman,Fayin Li,Azriel Rosenfeld,Michael J. Schoelles,Christian D. Schunn,Harry Wechsler +7 more
TL;DR: Technology and tools for intelligent human-computer interaction (IHCI) in which human cognitive, perceptual, motor and affective factors are modeled and used to adapt the H-C interface are described.
Journal ArticleDOI
Open set face recognition using transduction
Fayin Li,Harry Wechsler +1 more
TL;DR: Open set TCM-kNN (transduction confidence machine-k nearest neighbors), suitable for multiclass authentication operational scenarios that have to include a rejection option for classes never enrolled in the gallery, is shown to be suitable for PSEI (pattern specific error inhomogeneities) error analysis in order to identify difficult to recognize faces.
Proceedings ArticleDOI
Vision based topological Markov localization
Jana Kosecka,Fayin Li +1 more
TL;DR: This paper compares the recognition performance using global image histograms as well as local scale-invariant features as image descriptors, demonstrate their strengths and weaknesses and shows how to model the spatial relationships between individual locations by a Hidden Markov Model.