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Sinjini Mitra

Researcher at California State University, Fullerton

Publications -  74
Citations -  683

Sinjini Mitra is an academic researcher from California State University, Fullerton. The author has contributed to research in topics: Biometrics & Facial recognition system. The author has an hindex of 11, co-authored 69 publications receiving 596 citations. Previous affiliations of Sinjini Mitra include Procter & Gamble & University of Southern California.

Papers
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Journal ArticleDOI

Facial asymmetry quantification for expression invariant human identification

Yanxi Liu, +3 more
- 01 Jul 2003 - 
TL;DR: The findings show that the asymmetry measures of automatically selected facial regions capture individual differences that are relatively stable to facial expression variations, and a synergy is achieved by combining facial asymmetry information with conventional EigenFace and FisherFace methods.
Journal ArticleDOI

Measurement of the weak mixing angle using the forward-backward asymmetry of Drell-Yan events in pp collisions at 8 TeV.

Albert M. Sirunyan, +2260 more
- 01 Sep 2018 - 
TL;DR: With more events and new analysis techniques, including constraints obtained on the parton distribution functions from the measured forward–backward asymmetry, the statistical and systematic uncertainties are significantly reduced relative to previous CMS measurements.
Proceedings Article

Modeling learning patterns of students with a tutoring system using Hidden Markov Models

Carole R. Beal, +2 more
TL;DR: It is suggested that modeling learner engagement may help to increase the effectiveness of intelligent tutoring systems since it was observed that engagement trajectories were not predicted by prior math achievement of students.
Proceedings ArticleDOI

Local facial asymmetry for expression classification

Sinjini Mitra, +1 more
TL;DR: Using 2D facial expression images, the effectiveness of automatically selected local facial asymmetry for expression recognition is shown and a comparison of discriminative local facialymmetry features for expression classification versus human identification is given.
Proceedings ArticleDOI

Gaussian mixture models based on the frequency spectra for human identification and illumination classification

Sinjini Mitra, +1 more
TL;DR: This paper introduces a model-based approach using Gaussian mixture models (GMM) based on phase for performing human identification and demonstrates that GMM based on the Fourier domain magnitude is effective for illumination normalization, so that near perfect identification is obtained using the reconstructed illumination-free images.