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   https://doi.org/10.1109/CVPR52688.2022.01993  

   Wang, Zhen; Ba, Yunhao; Chari, Pradyumna; Bozkurt, Oyku Deniz; Brown, Gianna; Patwa, Parth; Vaddi, Niranjan; Jalilian, Laleh; Kadambi, Achuta (June 2022, Synthetic Generation of Face Videos with Plethysmograph Physiology)
2. iBall: Augmenting Basketball Videos with Gaze-moderated Embedded Visualizations

   https://doi.org/10.1145/3544548.3581266  

   Chen, Zhutian; Yang, Qisen; Shan, Jiarui; Lin, Tica; Beyer, Johanna; Xia, Haijun; Pfister, Hanspeter (January 2023, CHI Conference on Human Factors in Computing Systems)

   We present iBall, a basketball video-watching system that leverages gaze-moderated embedded visualizations to facilitate game understanding and engagement of casual fans. Video broadcasting and online video platforms make watching basketball games increasingly accessible. Yet, for new or casual fans, watching basketball videos is often confusing due to their limited basketball knowledge and the lack of accessible, on-demand information to resolve their confusion. To assist casual fans in watching basketball videos, we compared the game-watching behaviors of casual and die-hard fans in a formative study and developed iBall based on the findings. iBall embeds visualizations into basketball videos using a computer vision pipeline, and automatically adapts the visualizations based on the game context and users’ gaze, helping casual fans appreciate basketball games without being overwhelmed. We confirmed the usefulness, usability, and engagement of iBall in a study with 16 casual fans, and further collected feedback from another 8 die-hard fans. 

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3. Face Presentation Attack with Latex Masks in Multispectral Videos

   https://doi.org/10.1109/CVPRW.2017.40  

   Agarwal, Akshay; Yadav, Daksha; Kohli, Naman; Singh, Richa; Vatsa, Mayank; Noore, Afzel (July 2017, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop.)

   Face recognition systems are susceptible to presentation attacks such as printed photo attacks, replay attacks, and 3D mask attacks. These attacks, primarily studied in visible spectrum, aim to obfuscate or impersonate a person’s identity. This paper presents a unique multispectral video face database for face presentation attack using latex and paper masks. The proposed Multispectral Latex Mask based Video Face Presentation Attack (MLFP) database contains 1350 videos in visible, near infrared, and thermal spectrums. Since the database consists of videos of subjects without any mask as well as wearing ten different masks, the effect of identity concealment is analyzed in each spectrum using face recognition algorithms. We also present the performance of existing presentation attack detection algorithms on the proposed MLFP database. It is observed that the thermal imaging spectrum is most effective in detecting face presentation attacks. 

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