Mihai Bâce

Institute for Visualisation and Interactive Systems, University of Stuttgart

  Pfaffenwaldring 5a, 70569 Stuttgart, Germany University of Stuttgart, SimTech Building, Room 01.029

Biography

Mihai Bâce is a post-doctoral researcher in the Perceptual User Interfaces group since December 2020. He did his PhD at ETH Zurich, Switzerland, in the Distributed Systems Group at the Institute for Intelligent Interactive Systems. He studied for his Master's degree at École Polytechnique Fédérale de Lausanne (EPFL) and for his Bachelor's at the Technical University of Cluj-Napoca in Romania. His research interests are in Human-Computer Interaction with a focus on computational aspects and has influences from both Machine Learning and Computer Vision.

Publications

1. 

   How far are we from quantifying visual attention in mobile HCI?

   Mihai Bâce, Sander Staal, Andreas Bulling

   IEEE Pervasive Computing, 19(2), pp. 46-55, 2020.

   Abstract Links BibTeX Project

   With an ever-increasing number of mobile devices competing for attention, quantifying when, how often, or for how long users look at their devices has emerged as a key challenge in mobile human-computer interaction. Encouraged by recent advances in automatic eye contact detection using machine learning and device-integrated cameras, we provide a fundamental investigation into the feasibility of quantifying overt visual attention during everyday mobile interactions. We discuss the main challenges and sources of error associated with sensing visual attention on mobile devices in the wild, including the impact of face and eye visibility, the importance of robust head pose estimation, and the need for accurate gaze estimation. Our analysis informs future research on this emerging topic and underlines the potential of eye contact detection for exciting new applications towards next-generation pervasive attentive user interfaces.

   doi: 10.1109/MPRV.2020.2967736

   Paper: bace20_pcm.pdf

   @article{bace20_pcm, title = {How far are we from quantifying visual attention in mobile HCI?}, author = {Bâce, Mihai and Staal, Sander and Bulling, Andreas}, journal = {IEEE Pervasive Computing}, year = {2020}, volume = {19}, number = {2}, doi = {10.1109/MPRV.2020.2967736}, pages = {46-55} }

1. 

   Combining Gaze Estimation and Optical Flow for Pursuits Interaction

   Mihai Bâce, Vincent Becker, Chenyang Wang, Andreas Bulling

   Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA), pp. 1-10, 2020.

   Abstract Links BibTeX Project Best paper award

   Pursuit eye movements have become widely popular because they enable spontaneous eye-based interaction. However, existing methods to detect smooth pursuits require special-purpose eye trackers. We propose the first method to detect pursuits using a single off-the-shelf RGB camera in unconstrained remote settings. The key novelty of our method is that it combines appearance-based gaze estimation with optical flow in the eye region to jointly analyse eye movement dynamics in a single pipeline. We evaluate the performance and robustness of our method for different numbers of targets and trajectories in a 13-participant user study. We show that our method not only outperforms the current state of the art but also achieves competitive performance to a consumer eye tracker for a small number of targets. As such, our work points towards a new family of methods for pursuit interaction directly applicable to an ever-increasing number of devices readily equipped with cameras.

   doi: 10.1145/3379155.3391315

   Paper: bace20_etra.pdf

   @inproceedings{bace20_etra, title = {Combining Gaze Estimation and Optical Flow for Pursuits Interaction}, author = {Bâce, Mihai and Becker, Vincent and Wang, Chenyang and Bulling, Andreas}, year = {2020}, booktitle = {Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA)}, doi = {10.1145/3379155.3391315}, pages = {1-10} }
2. 

   Quantification of Users’ Visual Attention During Everyday Mobile Device Interactions

   Mihai Bâce, Sander Staal, Andreas Bulling

   Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1–14, 2020.

   Abstract Links BibTeX Project

   We present the first real-world dataset and quantitative evaluation of visual attention of mobile device users in-situ, i.e. while using their devices during everyday routine. Understanding user attention is a core research challenge in mobile HCI but previous approaches relied on usage logs or self-reports that are only proxies and consequently do neither reflect attention completely nor accurately. Our evaluations are based on Everyday Mobile Visual Attention (EMVA) – a new 32-participant dataset containing around 472 hours of video snippets recorded over more than two weeks in real life using the front-facing camera as well as associated usage logs, interaction events, and sensor data. Using an eye contact detection method, we are first to quantify the highly dynamic nature of everyday visual attention across users, mobile applications, and usage contexts. We discuss key insights from our analyses that highlight the potential and inform the design of future mobile attentive user interfaces.

   doi: 10.1145/3313831.3376449

   Paper: bace20_chi.pdf

   Dataset: http://www.emva-dataset.org/

   News: https://ethz.ch/en/news-and-events/eth-news/news/2020/09/our-actual-attention-is-now-measurable.html

   Video: https://www.youtube.com/watch?v=SzLn3LujIqw

   @inproceedings{bace20_chi, title = {Quantification of Users' Visual Attention During Everyday Mobile Device Interactions}, author = {Bâce, Mihai and Staal, Sander and Bulling, Andreas}, year = {2020}, pages = {1--14}, booktitle = {Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI)}, doi = {10.1145/3313831.3376449}, news = {https://ethz.ch/en/news-and-events/eth-news/news/2020/09/our-actual-attention-is-now-measurable.html}, video = {https://www.youtube.com/watch?v=SzLn3LujIqw} }

1. 

   How far are we from quantifying visual attention in mobile HCI?

   Mihai Bâce, Sander Staal, Andreas Bulling

   arXiv:1907.11106, pp. 1–7, 2019.

   Abstract Links BibTeX Project

   With an ever-increasing number of mobile devices competing for our attention, quantifying when, how often, or for how long users visually attend to their devices has emerged as a core challenge in mobile human-computer interaction. Encouraged by recent advances in automatic eye contact detection using machine learning and device-integrated cameras, we provide a fundamental investigation into the feasibility of quantifying visual attention during everyday mobile interactions. We identify core challenges and sources of errors associated with sensing attention on mobile devices in the wild, including the impact of face and eye visibility, the importance of robust head pose estimation, and the need for accurate gaze estimation. Based on this analysis, we propose future research directions and discuss how eye contact detection represents the foundation for exciting new applications towards next-generation pervasive attentive user interfaces.

   Paper: bace19_arxiv_2.pdf

   Paper Access: https://arxiv.org/abs/1907.11106

   @techreport{bace19_arxiv_2, title = {How far are we from quantifying visual attention in mobile HCI?}, author = {Bâce, Mihai and Staal, Sander and Bulling, Andreas}, year = {2019}, pages = {1--7}, url = {https://arxiv.org/abs/1907.11106} }
2. 

   Accurate and Robust Eye Contact Detection During Everyday Mobile Device Interactions

   Mihai Bâce, Sander Staal, Andreas Bulling

   arXiv:1907.11115, pp. 1–12, 2019.

   Abstract Links BibTeX Project

   Quantification of human attention is key to several tasks in mobile human-computer interaction (HCI), such as predicting user interruptibility, estimating noticeability of user interface content, or measuring user engagement. Previous works to study mobile attentive behaviour required special-purpose eye tracking equipment or constrained users’ mobility. We propose a novel method to sense and analyse visual attention on mobile devices during everyday interactions. We demonstrate the capabilities of our method on the sample task of eye contact detection that has recently attracted increasing research interest in mobile HCI. Our method builds on a state-of-the-art method for unsupervised eye contact detection and extends it to address challenges specific to mobile interactive scenarios. Through evaluation on two current datasets, we demonstrate significant performance improvements for eye contact detection across mobile devices, users, or environmental conditions. Moreover, we discuss how our method enables the calculation of additional attention metrics that, for the first time, enable researchers from different domains to study and quantify attention allocation during mobile interactions in the wild.

   Paper: bace19_arxiv.pdf

   Paper Access: https://arxiv.org/abs/1907.11115

   @techreport{bace19_arxiv, title = {Accurate and Robust Eye Contact Detection During Everyday Mobile Device Interactions}, author = {Bâce, Mihai and Staal, Sander and Bulling, Andreas}, year = {2019}, pages = {1--12}, url = {https://arxiv.org/abs/1907.11115} }