Heysem Kaya

Dr. Heysem Kaya

Utrecht University, Netherlands
orcid

Research Focus & Metrics

Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2017 in Image and Vision Computing
Verified
Journal Article
Closed access
Heysem KayaHeysem Kaya
Heysem Kaya
FGFurkan Gürpınar
Furkan Gürpınar
ASAlbert Ali Salah
Albert Ali Salah

15 October 2018
Verified
Conference Paper
Open access
FRFabien Ringeval
Fabien Ringeval
BSBjörn Schuller
Björn Schuller
MVMichel Valstar
Michel Valstar
RCRoddy Cowie
Roddy Cowie
Heysem KayaHeysem Kaya
Heysem Kaya
MPMaja Pantic
Maja Pantic

26 November 2019 in Turkish Journal of Electrical Engineering and Computer Sciences
Verified
Journal Article
Open access
Heysem KayaHeysem Kaya
Heysem Kaya
PTPinar TÜFEKCİ
Pinar TÜFEKCİ
EUErdinç Uzun
Erdinç Uzun

14 February 2020 in IEEE Transactions on Affective Computing
Verified
Journal Article
Closed access
HEHugo Jair Escalante
Hugo Jair Escalante
Heysem KayaHeysem Kaya
Heysem Kaya
ASAlbert Ali Salah
Albert Ali Salah
Sergio EscaleraSergio Escalera
Sergio Escalera
YGYagmur Gucluturk
Yagmur Gucluturk
RLRob van Lier
Rob van Lier
Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future.

30 August 2021
Verified
Conference Paper
Closed access
BSBjörn W. Schuller
Björn W. Schuller
ABAnton Batliner
Anton Batliner
CBChristian Bergler
Christian Bergler
CMCecilia Mascolo
Cecilia Mascolo
JHJing Han
Jing Han
CKCasper S. Kaandorp
Casper S. Kaandorp

7 November 2014
Verified
Conference Paper
Open access
Heysem KayaHeysem Kaya
Heysem Kaya
FÇFazilet Çilli
Fazilet Çilli
ASAlbert Ali Salah
Albert Ali Salah
This paper presents our work on ACM MM Audio Visual Emotion Corpus 2014 (AVEC 2014) using the baseline features in accordance with the challenge protocol. For prediction, we use Canonical Correlation Analysis (CCA) in affect sub-challenge (ASC) and Moore-Penrose generalized inverse (MPGI) in depression sub-challenge (DSC). The video baseline provides histograms of Local Gabor Binary Patterns from Three Orthogonal Planes (LGBP-TOP) features. Based on our preliminary experiments on AVEC 2013 challenge data, we focus on the inner facial regions that correspond to eyes and mouth area. We obtain an ensemble of regional linear regressors via CCA and MPGI. We also enrich the 2014 baseline set with Local Phase Quantization (LPQ) features extracted using Intraface toolkit detected/tracked faces. Combining both representations in a CCA ensemble approach, on the challenge test set we reach an average Pearson's Correlation Coefficient (PCC) of 0.3932, outperforming the ASC test set baseline PCC of 0.1966. On the DSC, combining modality specific MPGI based ensemble systems, we reach 9.61 Root Mean Square Error (RMSE).