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

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2. Targeted memory reactivation of face-name learning depends on ample and undisturbed slow-wave sleep

   https://doi.org/10.1038/s41539-021-00119-2  

   Whitmore, Nathan W.; Bassard, Adrianna M.; Paller, Ken A. (January 2022, npj Science of Learning)

   Abstract Face memory, including the ability to recall a person’s name, is of major importance in social contexts. Like many other memory functions, it may rely on sleep. We investigated whether targeted memory reactivation during sleep could improve associative and perceptual aspects of face memory. Participants studied 80 face-name pairs, and then a subset of spoken names with associated background music was presented unobtrusively during a daytime nap. This manipulation preferentially improved name recall and face recognition for those reactivated face-name pairs, as modulated by two factors related to sleep quality; memory benefits were positively correlated with the duration of stage N3 sleep (slow-wave sleep) and negatively correlated with measures of sleep disruption. We conclude that (a) reactivation of specific face-name memories during sleep can strengthen these associations and the constituent memories, and that (b) the effectiveness of this reactivation depends on uninterrupted N3 sleep. 

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3. 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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4. 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)
5. Face Liveness Detection Competition (LivDet-Face) - 2024

   https://doi.org/10.1109/IJCB62174.2024.10744462  

   Igene, Lambert; Hossain, Afzal; Uddin_Chowdhury, Mohammad Zahir; Rezaie, Humaira; Rollins, Ayden; Dykes, Jesse; Vijaykumar, Rahul; Komaty, Alain; Marcel, Sébastien; Schuckers, Stephanie; et al (September 2024, IEEE)