Keywords (6)

Academic

Publications

Feature distribution modelling techniques for 3D face verification

Feature distribution modelling techniques for 3D face verification,10.1016/j.patrec.2010.01.029,Pattern Recognition Letters,Chris McCool,Jordi Sanchez

Edit

Feature distribution modelling techniques for 3D face verification (Citations: 2)


	BibTex \| RIS \| RefWorks Download

, ,

This paper shows that Hidden Markov models (HMMs) can be effectively applied to 3D face data. The examined HMM techniques are shown to be superior to a previously examined Gaussian mixture model (GMM) technique. Experiments conducted on the Face Recognition Grand Challenge database show that the Equal Error Rate can be reduced from 0.88% for the GMM technique to 0.36% for the best HMM approach.

Journal: Pattern Recognition Letters - PRL , vol. 31, no. 11, pp. 1324-1330, 2010

DOI: 10.1016/j.patrec.2010.01.029

Cumulative Annual

View Publication

( www.sciencedirect.com )

( www.informatik.uni-trier.de )

( dx.doi.org )

- ...If the log-likelihood score in Eq. (2) is higher than the threshold ( ε Λ> ), the claimed speaker state will be accepted, else rejected [11]...
Chien-Lin Huang, et al. Feature normalization and selection for robust speaker state recogniti...
- ...If the log-likelihood ratio is higher than the threshold θ Λ the claimed speaker will be accepted, otherwise rejected [12]...
Chien-Lin Huang, et al. UBM data selection for effective speaker modeling