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1. Face-Off: Adversarial Face Obfuscation

   Chandrasekaran, V ; Gao, C ; Tang, B ; Fawaz, K ; Jha, S ; Banerjee, S ( January 2021 , Proceedings on Privacy Enhancing Technologies)

   null (Ed.)
2. Face Liveness Detection Competition (LivDet-Face) - 2021

   https://doi.org/10.1109/IJCB52358.2021.9484359  

   Purnapatra, Sandip ; Smalt, Nic ; Bahmani, Keivan ; Das, Priyanka ; Yambay, David ; Mohammadi, Amir ; George, Anjith ; Bourlai, Thirimachos ; Marcel, Sebastien ; Schuckers, Stephanie ; et al ( August 2021 , 2021 IEEE International Joint Conference on Biometrics (IJCB))

   Liveness Detection (LivDet)-Face is an international competition series open to academia and industry. The competition’s objective is to assess and report state-of-the-art in liveness / Presentation Attack Detection (PAD) for face recognition. Impersonation and presentation of false samples to the sensors can be classified as presentation attacks and the ability for the sensors to detect such attempts is known as PAD. LivDet-Face 2021 * will be the first edition of the face liveness competition. This competition serves as an important benchmark in face presentation attack detection, offering (a) an independent assessment of the current state of the art in face PAD, and (b) a common evaluation protocol, availability of Presentation Attack Instruments (PAI) and live face image dataset through the Biometric Evaluation and Testing (BEAT) platform. The competition can be easily followed by researchers after it is closed, in a platform in which participants can compare their solutions against the LivDet-Face winners. 

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3. Face templates for the Chicago Face Database

   https://doi.org/10.3758/s13428-022-01830-7  

   Singh, Balbir ; Gambrell, Ashleigh ; Correll, Joshua ( April 2022 , Behavior Research Methods)
4. Unconstrained Face Detection and Open-Set Face Recognition Challenge

   https://doi.org/10.1109/BTAS.2017.8272759  

   Gunther, M. ; Hu, P. ; Herrmann, C. ; Chan, C. H. ; Jiang, M. ; Yang, S. ; Dhamija, A. R. ; Ramanan, D. ; Beyerer, J. ; Kittler, J. ; et al ( October 2017 , International Joint Conference on Biometrics)

   Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. While face detection has shown remarkable success in images collected from the web, surveillance cameras include more diverse occlusions, poses, weather conditions and image blur. Although face verification or closed-set face identification have surpassed human capabilities on some datasets, open-set identification is much more complex as it needs to reject both unknown identities and false accepts from the face detector. We show that unconstrained face detection can approach high detection rates albeit with moderate false accept rates. By contrast, open-set face recognition is currently weak and requires much more attention. 

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5. Traveling Bazaar: Portable Support for Face-to-Face Collaboration

   Vitiello, Rosanna ; Tiwari, Soham D. ; Murray, R. Charles ; Rosé, Carolyn ( January 2023 , International Collaboration toward Educational Innovation for All: International Society of the Learning Sciences (ISLS) Annual Meeting 2023)

   For nearly two decades, conversational agents have been used to structure group interactions in online chat-based environments. More recently, this form of dynamic support for collaborative learning has been extended to physical spaces using a combination of multimodal sensing technologies and instrumentation installed within a physical space. This demo extends the reach of dynamic support for collaboration still further through an application of what has recently been termed on-device machine learning, which enables a portable form of multimodal detection to trigger real-time responses. 

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