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Maximisation of mutual information for gait-based soft biometric classification using Gabor features
Maodi Hu; Yunhong Wang; Zhaoxiang Zhang
Source PublicationIET Biometrics
2012-04-12
Volume1Issue:1Pages:55-62
AbstractBesides 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.
KeywordFeature Extraction Gabor Filters Gait Analysis Gender Issues Hidden Markov Models Image Enhancement
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13236
Collection类脑智能研究中心
Corresponding AuthorMaodi Hu
Recommended Citation
GB/T 7714
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.
APA 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.
MLA 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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