TY - GEN
T1 - vUBM
T2 - 26th International Conference on Neural Information Processing, ICONIP 2019
AU - Tran, Huyen
AU - Tran, Dat
AU - Ma, Wanli
AU - Nguyen, Phuoc
PY - 2019/1/1
Y1 - 2019/1/1
N2 - EEG-based person authentication is an important means for modern biometrics. However EEG signals are well-known for small signal-to-noise ratio and have many factors of variation. These variations are caused by intrinsic factors, e.g. mental activity, mood, and health conditions, as well as extrinsic factors, e.g. sensor errors, electrode displacements, and user movements. These create complex variations of source signals going from inside our brain to the recording devices. We propose vUBM, a variational inference framework to learn a simple latent representation for complex data, facilitating authentication algorithms in the latent space. A variational universal background model is created for normalizing scores to further improve the performance. Extensive experiments show the advantages of our proposed framework.
AB - EEG-based person authentication is an important means for modern biometrics. However EEG signals are well-known for small signal-to-noise ratio and have many factors of variation. These variations are caused by intrinsic factors, e.g. mental activity, mood, and health conditions, as well as extrinsic factors, e.g. sensor errors, electrode displacements, and user movements. These create complex variations of source signals going from inside our brain to the recording devices. We propose vUBM, a variational inference framework to learn a simple latent representation for complex data, facilitating authentication algorithms in the latent space. A variational universal background model is created for normalizing scores to further improve the performance. Extensive experiments show the advantages of our proposed framework.
UR - http://www.scopus.com/inward/record.url?scp=85089609572&partnerID=8YFLogxK
UR - http://ajiips.com.au/iconip2019/index.html
UR - https://www.mendeley.com/catalogue/86de191d-6729-3140-9136-dca4da45496e/
U2 - 10.1007/978-3-030-36808-1_52
DO - 10.1007/978-3-030-36808-1_52
M3 - Conference contribution
AN - SCOPUS:85089609572
SN - 9783030368074
VL - 4
T3 - Communications in Computer and Information Science
SP - 478
EP - 485
BT - Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings
A2 - Gedeon, Tom
A2 - Wong, Kok Wai
A2 - Lee, Minho
PB - Springer
CY - Netherlands
Y2 - 12 December 2019 through 15 December 2019
ER -