H
Hazim Kemal Ekenel
Researcher at Istanbul Technical University
Publications - 231
Citations - 4571
Hazim Kemal Ekenel is an academic researcher from Istanbul Technical University. The author has contributed to research in topics: Facial recognition system & Convolutional neural network. The author has an hindex of 30, co-authored 215 publications receiving 3554 citations. Previous affiliations of Hazim Kemal Ekenel include Sabancı University & Boğaziçi University.
Papers
More filters
Journal ArticleDOI
Learn to synthesize and synthesize to learn
Behzad Bozorgtabar,Mohammad Saeed Rad,Hazim Kemal Ekenel,Jean-Philippe Thiran,Jean-Philippe Thiran +4 more
TL;DR: Compared to existing models, synthetic face images generated by the proposed attribute guided face image generation method present a good photorealistic quality on several face datasets and can be used for synthetic data augmentation, and improve the performance of the classifier used for facial expression recognition.
Proceedings ArticleDOI
Facial action unit detection using kernel partial least squares
Tobias Gehrig,Hazim Kemal Ekenel +1 more
TL;DR: This work proposes a framework for simultaneously detecting the presence of multiple facial action units using kernel partial least square regression (KPLS), which has the advantage of being easily extensible to learn more face related labels, while at the same time being computationally efficient.
Proceedings ArticleDOI
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis
TL;DR: In this paper, a new attribute guided face image synthesis model is proposed to perform a translation between multiple image domains using a single model, and the model can learn from synthetic faces by matching the feature distributions between different domains while preserving each domain's characteristics.
Journal ArticleDOI
Audio-visual perception of a lecturer in a smart seminar room
Rainer Stiefelhagen,Keni Bernardin,Hazim Kemal Ekenel,John McDonough,Kai Nickel,Michael Voit,Matthias Wölfel +6 more
TL;DR: This work presents a novel approach to track the lecturer based on visual and acoustic observations in a particle filter framework that does not require explicit triangulation of observations in order to estimate the 3D location of the lecturer, thus allowing for fast audio-visual tracking.
Proceedings ArticleDOI
Why is facial expression analysis in the wild challenging
Tobias Gehrig,Hazim Kemal Ekenel +1 more
TL;DR: It turns out that under close-to-real conditions, especially with co-occurring speech, it is hard even for humans to assign emotion labels to clips when only taking video into account, so the challenges for facial expression analysis in the wild are discussed.