S
Seyedmohammad Mavadati
Researcher at University of Denver
Publications - 23
Citations - 1017
Seyedmohammad Mavadati is an academic researcher from University of Denver. The author has contributed to research in topics: Facial expression & Autism. The author has an hindex of 10, co-authored 23 publications receiving 829 citations. Previous affiliations of Seyedmohammad Mavadati include Yazd University.
Papers
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Journal ArticleDOI
DISFA: A Spontaneous Facial Action Intensity Database
TL;DR: To meet the need for publicly available corpora of well-labeled video, the Denver intensity of spontaneous facial action database is collected, ground-truthed, and prepared for distribution.
Proceedings ArticleDOI
Social risk and depression: Evidence from manual and automatic facial expression analysis
Jeffrey M. Girard,Jeffrey F. Cohn,Mohammad H. Mahoor,Seyedmohammad Mavadati,Dean P. Rosenwald +4 more
TL;DR: The finding that automatic facial expression analysis was both consistent with manual coding and produced the same pattern of depression effects suggests that automatic Facial expression analysis may be ready for use in behavioral and clinical science.
Patent
Analysis of image content with associated manipulation of expression presentation
TL;DR: In this paper, facial features are extracted from the identified faces and facial landmarks are translated into a representative icon, where the translation is based on classifiers, and the selected emoji can be static, animated, or cartoon representations of emotion.
Proceedings ArticleDOI
Automatic detection of non-posed facial action units
TL;DR: This paper introduces a novel spontaneous facial expression database called DISFA, which contains videos of 27 young adults, expressing non-posed facial expressions and presents an automatic system which can detect facial AUs described by FACS, and compared different facial representation techniques and classifiers for automatic AU detection.
Patent
Image analysis for attendance query evaluation
TL;DR: In this article, facial evaluation is performed on one or more videos captured from an individual viewing a display, and the images are evaluated to determine whether the display was viewed by the individual.