ABSTRACT
Facial Expressions are valuable data sources for advanced Human-Vehicle Interaction designs. However, existing works always consider the whole facial expressions as input, which restricts the design space for detailed optimizations. In this work, we make the hypothesis that facial expressions can exhibit significant variations during the driving procedure. Our goal in this work-in-progress is to justify this hypothesis, by performing detailed characterizations on the drivers’ facial expressions. To this end, we leverage Local Binary Fitting, a novel mechanism for selecting representative feature points from facial images on the fly, for our characterizations. Our characterizations reveal that, among six major components of facial feature points, there are significant variations of correlations with a certain vehicle status (i.e. Vehicle Speed), in terms of (1) the time spots during the driving procedure; and (2) the gender of the drivers. We believe our works can serve as a starting point to incorporate the characteristics of our findings with a great amount of adaptive and personalized Human-Vehicle Interaction designs.
- Riza Alp Guler, George Trigeorgis, Epameinondas Antonakos, Patrick Snape, Stefanos Zafeiriou, and Iasonas Kokkinos. 2017. Densereg: Fully convolutional dense shape regression in-the-wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 6799–6808.Google Scholar
- A-Nasser Ansari, Mohamed Abdel-Mottaleb, and Mohammad H Mahoor. 2007. 3D face mesh modeling from range images for 3D face recognition. In 2007 IEEE International Conference on Image Processing, Vol. 4. IEEE, IV–509.Google ScholarCross Ref
- Hua Cai and Yingzi Lin. 2011. Modeling of operators’ emotion and task performance in a virtual driving environment. International Journal of Human-Computer Studies 69, 9 (2011), 571–586.Google ScholarDigital Library
- Hua Gu, Guangda Su, and Cheng Du. 2003. Feature points extraction from faces. Image and vision computing NZ 26 (2003), 154–158.Google Scholar
- Zhentao Huang, Rongze Li, Wangkai Jin, Zilin Song, Yu Zhang, Xiangjun Peng, and Xu Sun. 2020. Face2Multi-modal: In-vehicle multi-modal predictors via facial expressions. In 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 30–33.Google ScholarDigital Library
- Wangkai Jin, Xiaoxing Ming, Zilin Song, Zeyu Xiong, and Xiangjun Peng. 2021. Towards Emulating Internet-of-Vehicles on a Single Machine. In Adjunct Proceedings of the 13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2021. ACM.Google ScholarDigital Library
- Hang-Bong Kang. 2013. Various approaches for driver and driving behavior monitoring: A review. In Proceedings of the IEEE International Conference on Computer Vision Workshops. 616–623.Google ScholarDigital Library
- Yury Kartynnik, Artsiom Ablavatski, Ivan Grishchenko, and Matthias Grundmann. 2019. Real-time facial surface geometry from monocular video on mobile GPUs. arXiv preprint arXiv:1907.06724(2019).Google Scholar
- Rolf M Koch, Markus H Gross, Friedrich R Carls, Daniel F von Büren, George Fankhauser, and Yoav IH Parish. 1996. Simulating facial surgery using finite element models. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques. 421–428.Google ScholarDigital Library
- Xiangjun Peng, Zhentao Huang, and Xu Sun. 2020. Building BROOK: A Multi-modal and Facial Video Database for Human-Vehicle Interaction Research. the 1st Workshop of Speculative Designs for Emergent Personal Data Trails: Signs, Signals and Signifiers, co-located with the 2020 CHI Conference on Human Factors in Computing Systems, (CHI) abs/2005.08637 (2020). arxiv:2005.08637https://arxiv.org/abs/2005.08637Google Scholar
- Zilin Song, Shuolei Wang, Weikai Kong, Xiangjun Peng, and Xu Sun. 2019. First attempt to build realistic driving scenes using video-to-video synthesis in OpenDS framework. In Adjunct Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2019, Utrecht, The Netherlands, September 21-25, 2019. ACM, 387–391. https://doi.org/10.1145/3349263.3351497Google ScholarDigital Library
- Xu Sun, Jingpeng Li, Pinyan Tang, Siyuan Zhou, Xiangjun Peng, Hao Nan Li, and Qingfeng Wang. 2020. Exploring Personalised Autonomous Vehicles to Influence User Trust. Cogn. Comput. 12, 6 (2020), 1170–1186. https://doi.org/10.1007/s12559-020-09757-xGoogle ScholarCross Ref
- Yu Zhang, Wangkai Jin, Zeyu Xiong, Zhihao Li, Yuyang Liu, and Xiangjun Peng. 2021. Demystifying Interactions Between Driving Behaviors and Styles Through Self-clustering Algorithms. In International Conference on Human-Computer Interaction. Springer, 335–350.Google Scholar
Recommendations
Face Recognition Through Different Facial Expressions
Face recognition has become an accessible issue for experts as well as ordinary people as it is a focal non-interfering biometric modality. In this paper, we introduced a new approach to perform face recognition under varying facial expressions. The ...
Determining facial expressions in real time
ICCV '95: Proceedings of the Fifth International Conference on Computer VisionWe suggest an approach to describing and tracking the deformation of facial features. We concentrate on the mouth since its shape is important in detecting emotion. However, we believe that our system could be extended to deal with other facial ...
Recognising facial expressions in video sequences
We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of ...
Comments