|Maximisation of mutual information for gait-based soft biometric classification using Gabor features|
|Maodi Hu; Yunhong Wang; Zhaoxiang Zhang
|Source Publication||IET Biometrics
|Abstract||Besides identity, soft biometric characteristics, such as gender and age can also be derived from gait patterns. With Gabor enhancement, supervised learning and temporal modelling, the authors present a robust framework to achieve state-of-the-art classification accuracy for both gender and age. Gabor filter and maximisation of mutual information are used to extract low-dimensional features, whereas Bayes rules based on hidden Markov models (HMMs) are adopted for soft biometric classification. The multi-view soft biometric classification problem is defined as two different cases, saying, one-to-one view and many-to-one view, according to the number of available gallery views. In case more than one gallery view is available, the multi-view soft biometric classification problem is hierarchically solved with a view-related population HMM, in which the estimated view angle is treated as the intermediate result in the first stage. Performance has been evaluated on benchmark databases, which verify the advantages of the proposed algorithm.|
Hidden Markov Models
|Corresponding Author||Maodi Hu|
Maodi Hu,Yunhong Wang,Zhaoxiang Zhang. Maximisation of mutual information for gait-based soft biometric classification using Gabor features[J]. IET Biometrics,2012,1(1):55-62.
Maodi Hu,Yunhong Wang,&Zhaoxiang Zhang.(2012).Maximisation of mutual information for gait-based soft biometric classification using Gabor features.IET Biometrics,1(1),55-62.
Maodi Hu,et al."Maximisation of mutual information for gait-based soft biometric classification using Gabor features".IET Biometrics 1.1(2012):55-62.
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