Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Simge Akay"'
Publikováno v:
Learning Control ISBN: 9780128223147
Although face analysis algorithms have changed over the decades, almost in every face-related algorithm it is still usually the case that the order of the problem solving algorithm is the same. The analysis task is relatively easy on frontal and clea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::59575c3155e51df64d595cc47b438464
https://doi.org/10.1016/b978-0-12-822314-7.00010-9
https://doi.org/10.1016/b978-0-12-822314-7.00010-9
Autor:
Nafiz Arica, Simge Akay
In this study, we develop a deep learning-based stacking scheme to detect facial action units (AU) in video data. Given a sequence of video frames, it combines multiple cues extracted from the AU detectors employing in frame, segment, and transition
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d44d7362096be64addcbb0d48581c5f
https://aperta.ulakbim.gov.tr/record/237506
https://aperta.ulakbim.gov.tr/record/237506
Autor:
Simge Akay, Basim Alghabashi, Mohamed Al Mashrgy, Nafiz Arica, Zeinab Arjmandiasl, Muhammad Azam, B. Balasingam, Jamal Bentahar, F. Biondi, Aaron Boda, Nizar Bouguila, Duygu Cakir, Mark Green, Sorin Grigorescu, Baoxin Hu, Xishi Huang, M. Khalghollah, Howard Li, C.J.B. Macnab, Narges Manouchehri, Jun Meng, Afshin Rahimi, P. Ramakrishnan, Jing Ren, Jianguo Wang, Jin Wang
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::79626e32df85d4b5d107eafae2610d8b
https://doi.org/10.1016/b978-0-12-822314-7.00005-5
https://doi.org/10.1016/b978-0-12-822314-7.00005-5
Autor:
Nafiz Arica, Simge Akay
Publikováno v:
SIU
The detection of facial action unit is one of the most important sources for describing facial expressions. Some reasons such as facial expressions in the video, changes in the environment, different posed face images make it more difficult to detect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66460064c6cc8a78ccbb5c4bdaa0e11d
https://doi.org/10.1109/siu.2018.8404350
https://doi.org/10.1109/siu.2018.8404350
Publikováno v:
SIU
In this study we propose a new method for Likert scale questionnary data analysis using auto-encoders. The proposed method extracts the patterns, which maximally activate the neurons of the hidden layer in the auto-encoder, trained by the questionnar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::40dd2f4931f103842374ccf1e44d8e87
https://doi.org/10.1109/siu.2018.8404485
https://doi.org/10.1109/siu.2018.8404485