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	Periocular Recognition using Unsupervised Convolutional RBM Feature Learning
	Nie, Lei; Kumar, Ajay; Zhan, Song
	2014
会议名称	Proceedings - International Conference on Pattern Recognition
会议地点	瑞典
英文摘要	Automated and accurate biometrics identification using periocular imaging has wide range of applications from human surveillance to improving performance for iris recognition systems, especially under less-constrained imaging environment. Restricted Boltzmann Machine is a generative stochastic neural network that can learn the probability distribution over its set of inputs. As a convolutional version of Restricted Boltzman Machines, CRBM aim to accommodate large image sizes and greatly reduce the computational burden. However in the best of our knowledge, the unsupervised feature learning methods have not been explored in biometrics area except for the face recognition. This paper explores the effectiveness of CRBM model for the periocular recognition. We perform experiments on periocular image database from the largest number of subjects (300 subjects as test subjects) and simultaneously exploit key point features for improving the matching accuracy. The experimental results are presented on publicly available database, the Ubripr database, and suggest effectiveness of RBM feature learning for automated periocular recognition with the large number of subjects. The results from the investigation in this paper also suggest that the supervised metric learning can be effectively used to achieve superior performance than the conventional Euclidean distance metric for the periocular identification.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/5619]
专题	深圳先进技术研究院_集成所
作者单位	2014
推荐引用方式 GB/T 7714	Nie, Lei,Kumar, Ajay,Zhan, Song. Periocular Recognition using Unsupervised Convolutional RBM Feature Learning[C]. 见:Proceedings - International Conference on Pattern Recognition. 瑞典.

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