Titlebook: Audio-and Video-Based Biometric Person Authentication; 4th International Co Josef Kittler,Mark S. Nixon Conference proceedings 2003 Springe

locus-ceruleus

GUMP

疼死我了

Expression-Invariant 3D Face Recognition a representation of the facial surface, invariant to isometric deformations, such as those resulting from different expressions and postures of the face. The obtained geometric invariants allow mapping 2D facial texture images into special images that incorporate the 3D geometry of the face. These

地名詞典

Constrain

A Bayesian Network Approach for Combining Pitch and Reliable Spectral Envelope Features for Robust S data theory and Bayesian networks. This approach integrates high-level information concerning the reliability of pitch and spectral envelope features in missing feature compensation process in order to increase the performance of Gaussian mixture models (GMM) of speakers. In this paper, a Bayesian

FIR

Cluster-Dependent Feature Transformation for Telephone-Based Speaker Verification available during the training phase. The technique combines a cluster selector with cluster-dependent feature transformations to reduce the acoustic mismatches among different handsets. Specifically, a GMM-based cluster selector is trained to identify the cluster that best represents the handset us

DAFT

Searching through a Speech Memory for Text-Independent Speaker Verificationc feature vectors. Previous studies have shown that phonemes have different discriminant power for the speaker verification task. In order to better exploit these differences, it seems reasonable to segment the speech in distinct speech classes and carry out the speaker modeling for each class separ

痛打

LUT-Based Adaboost for Gender Classification better in correct rate but are more computation intensive while Adaboost ones are much faster with slightly worse performance. For possible real-time applications the Adaboost method seems a better choice. However, the existing Adaboost algorithms take simple threshold weak classifiers, which are t

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Independent Component Analysis and Support Vector Machine for Face Feature Extractionlization context. The goal is to find a better space where project the data in order to build ten different face-feature classi fiers that are robust to illumination variations and bad environment conditions. The method was tested on the BANCA database, in different scenarios: controlled conditions,

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