The Importance Distribution of Drivers’ Facial Expressions Varies over Time!

Wang, Jiahao; Xiong, Zeyu; Duan, Yicun; Liu, Junyu; Song, Zilin; Peng, Xiangjun

doi:10.1145/3473682.3480283

Cited by 11 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To unify this situation, we developed an algorithm of eye feature point localization for each image and resized it to the same size. There are some existing works related to facial feature point localization that achieve high accuracy [44,79,85], however, none of them focuses on the eye area only. To enhance the portability of the deployment, we referred to the face feature points localization and iris landmark detection methods (FaceMesh) [72] on Google MediaPipe [47].…”

Section: Prerequisitementioning

confidence: 99%

“It is hard to remove from my eye”: Design Makeup Residue Visualization System for Chinese Traditional Opera (Xiqu) Performers

Xiong,

Fu,

Zhu

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

Self Cite

View full text Add to dashboard Cite

Chinese traditional opera (Xiqu) performers often experience skin problems due to the long-term use of heavy-metal-laden face paints. To explore the current skincare challenges encountered by Xiqu performers, we conducted an online survey (N=136) and semistructured interviews (N=15) as a formative study. We found that incomplete makeup removal is the leading cause of human-induced skin problems, especially the difficulty in removing eye makeup. Therefore, we proposed EyeVis, a prototype that can visualize the residual eye makeup and record the time make-up was worn by Xiqu performers. We conducted a 7-day deployment study (N=12) to evaluate EyeVis. Results indicate that EyeVis helps to increase Xiqu performers' awareness about removing makeup, as well as boosting their confidence and security in skincare. Overall, this work also provides implications for studying the work of people who wear makeup on a daily basis, and helps to promote and preserve the intangible cultural heritage of practitioners.CCS Concepts: • Human-centered computing → Empirical studies in HCI; Assistive technologies.

show abstract

Section: Prerequisitementioning

confidence: 99%

“It is hard to remove from my eye”: Design Makeup Residue Visualization System for Chinese Traditional Opera (Xiqu) Performers

Xiong,

Fu,

Zhu

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…conditions, lights, etc. ), in-vehicle sensors for drivers [5] and in-vehicle monitors of driving statistics [17,25]; and ➋ for Processing-Outside-Vehicles, other applications may need assistance from centralized servers within IoV , due to either the application requirements or computational powers. There are many examples of applications, which require data aggregations in centralized servers.…”

Section: Classifying Application Of Iov : Processing Inside or Outsid...mentioning

confidence: 99%

Characterizing Differentially-Private Techniques in the Era of Internet-of-Vehicles

Duan¹,

Liu²,

Jin³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Recent developments of advanced Human-Vehicle Interactions rely on the concept Internet-of-Vehicles (IoV ), to achieve large-scale communications and synchronizations of data in practice. The concept of IoV is highly similar to a distributed system, where each vehicle is considered as a node and all nodes are grouped with a centralized server. In this manner, the concerns of data privacy are significant since all vehicles collect, process and share personal statistics (e.g. multi-modal, driving statuses and etc.). Therefore, it's important to understand how modern privacy-preserving techniques suit for IoV .We present the most comprehensive study to characterize modern privacy-preserving techniques for IoV to date. We focus on Differential Privacy (DP), a representative set of mathematicallyguaranteed mechanisms for both privacy-preserving processing and sharing on sensitive data. The purpose of our study is to demystify the tradeoffs of deploying DP techniques, in terms of service quality. We first characterize representative privacy-preserving processing mechanisms, enabled by advanced DP approaches. Then we perform a detailed study of an emerging in-vehicle, Deep-Neural-Network-driven application, and study the upsides and downsides of DP for diverse types of data streams. Our study obtains 11 key findings and we highlight FIVE most significant observations from our detailed characterizations. We conclude that there are a large volume of challenges and opportunities for future studies, by enabling privacy-preserving IoV with low overheads for service quality.

show abstract

“…Though our works already demonstrate the potential, we believe there are still rooms for more powerful predictors(e.g. more detailed facial expression [25,32,45]) and customization-friendly predictors [48] inside vehicles. It also of great pracical values to integrate them into certain platforms (e.g., IoV Emulation Platform [27]) to improve the overall Quality-of-Service.…”

Section: Takeaways From Brook Dataset and Example Studiesmentioning

confidence: 99%

BROOK Dataset: A Playground for Exploiting Data-Driven Techniques in Human-Vehicle Interactive Designs

Jin¹,

Duan²,

Liu³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Emerging Autonomous Vehicles (AV) breed great potentials to exploit data-driven techniques for adaptive and personalized Human-Vehicle Interactions. However, the lack of high-quality and rich data supports limits the opportunities to explore the design space of data-driven techniques, and validate the effectiveness of concrete mechanisms. Our goal is to initialize the efforts to deliver the building block for exploring data-driven Human-Vehicle Interaction designs. To this end, we present BROOK dataset, a multi-modal dataset with facial video records. We first brief our rationales to build BROOK dataset. Then, we elaborate how to build the current version of BROOK dataset via a year-long study, and give an overview of the dataset. Next, we present three example studies using BROOK to justify the applicability of BROOK dataset. We also identify key learning lessons from building BROOK dataset, and discuss about how BROOK dataset can foster an extensive amount of follow-up studies. INTRODUCTIONRecent advances in data-driven techniques (e.g. Neural Networks) breed an extensive amount of opportunities, to enable adaptive and personalized Human-Vehicle Interaction (HVI). More interestingly, the emerging trends of Autonomous Vehicles relax the conventional burdens of driving, and in-vehicle drivers/passengers are capable to obtain better user experiences through more complex HVI. Incorporating data-driven approaches can greatly improve user experiences during driving processes. For instance, unobtrusive monitors of multi-modal statuses (i.e. by taking alternative data sources as input), rather than directly equipping biosensors, are more user-friendly as data sources for personalized interaction between drivers and vehicles. Therefore, combining data-driven techniques with HVI becomes promising in the near future, and relevant datasets become essential and highly demanded.Though there are already several datasets for HVI, there are four major limitations of existing datasets. First, the design purposes of existing datasets focus on a specific type of study purposes (e.g. Stress Detection [20], Driving Workload [40] and etc.), which naturally limits their potentials due to the narrowly-selected types of data streams. Second, existing datasets only collect information

show abstract

The Importance Distribution of Drivers’ Facial Expressions Varies over Time!

Cited by 11 publications

References 8 publications

“It is hard to remove from my eye”: Design Makeup Residue Visualization System for Chinese Traditional Opera (Xiqu) Performers

“It is hard to remove from my eye”: Design Makeup Residue Visualization System for Chinese Traditional Opera (Xiqu) Performers

Characterizing Differentially-Private Techniques in the Era of Internet-of-Vehicles

BROOK Dataset: A Playground for Exploiting Data-Driven Techniques in Human-Vehicle Interactive Designs

Contact Info

Product

Resources

About