Maximisation of mutual information for gait-based soft biometric classification using Gabor features

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	Maximisation of mutual information for gait-based soft biometric classification using Gabor features
	Maodi Hu; Yunhong Wang; Zhaoxiang Zhang
发表期刊	IET Biometrics
	2012-04-12
卷号	1 期号:1 页码:55-62
摘要	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.
关键词	Feature Extraction Gabor Filters Gait Analysis Gender Issues Hidden Markov Models Image Enhancement
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13236
专题	模式识别实验室
通讯作者	Maodi Hu
推荐引用方式 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.