Periocular recognition using unsupervised convolutional RBM feature learning

Lei Nie, Ajay Kumar Pathak, Song Zhan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

38 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
Number of pages6
ISBN (Electronic)9781479952083
Publication statusPublished - 1 Jan 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014


Conference22nd International Conference on Pattern Recognition, ICPR 2014


  • Biometrics
  • CRBM
  • Periocular Recognition
  • Supervised Metric learning
  • Unsupervised Feature Learning

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Periocular recognition using unsupervised convolutional RBM feature learning'. Together they form a unique fingerprint.

Cite this