TY - GEN
T1 - EFaR 2023
T2 - 2023 IEEE International Joint Conference on Biometrics, IJCB 2023
AU - Kolf, Jan Niklas
AU - Boutros, Fadi
AU - Elliesen, Jurek
AU - Theuerkauf, Markus
AU - Damer, Naser
AU - Alansari, Mohamad
AU - Hay, Oussama Abdul
AU - Alansari, Sara
AU - Javed, Sajid
AU - Werghi, Naoufel
AU - Grm, Klemen
AU - Struc, Vitomir
AU - Alonso-Fernandez, Fernando
AU - Diaz, Kevin Hernandez
AU - Bigun, Josef
AU - George, Anjith
AU - Ecabert, Christophe
AU - Shahreza, Hatef Otroshi
AU - Kotwal, Ketan
AU - Marcel, Sebastien
AU - Medvedev, Iurii
AU - Jin, Bo
AU - Nunes, Diogo
AU - Hassanpour, Ahmad
AU - Khatiwada, Pankaj
AU - Toor, Aafan Ahmad
AU - Yang, Bian
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.
AB - This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.
UR - http://www.scopus.com/inward/record.url?scp=85171755032&partnerID=8YFLogxK
U2 - 10.1109/IJCB57857.2023.10448917
DO - 10.1109/IJCB57857.2023.10448917
M3 - Conference contribution
AN - SCOPUS:85171755032
T3 - 2023 IEEE International Joint Conference on Biometrics, IJCB 2023
BT - 2023 IEEE International Joint Conference on Biometrics, IJCB 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 September 2023 through 28 September 2023
ER -