@conference {15918,
	title = {Sectored Random Projections for Cancelable Iris Biometrics},
	booktitle = {2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)},
	year = {2010},
	month = {2010/03//},
	pages = {1838 - 1841},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Privacy and security are essential requirements in practical biometric systems. In order to prevent the theft of biometric patterns, it is desired to modify them through revocable and non invertible transformations called Cancelable Biometrics. In this paper, we propose an efficient algorithm for generating a Cancelable Iris Biometric based on Sectored Random Projections. Our algorithm can generate a new pattern if the existing one is stolen, retain the original recognition performance and prevent extraction of useful information from the transformed patterns. Our method also addresses some of the drawbacks of existing techniques and is robust to degradations due to eyelids and eyelashes.},
	keywords = {biometric pattern, Biometrics, Cancelable Biometrics, cancelable iris biometrics, data mining, data privacy, Degradation, Eyelashes, Eyelids, Iris, iris recognition, pattern recognition, privacy, random processes, Random Projections, Robustness, sectored random projection, Secure Biometrics, Security, security of data},
	isbn = {978-1-4244-4295-9},
	doi = {10.1109/ICASSP.2010.5495383},
	author = {Pillai,J.K. and Patel, Vishal M. and Chellapa, Rama and Ratha,N. K}
}
@conference {15921,
	title = {Sparse representations and Random Projections for robust and cancelable biometrics},
	booktitle = {Control Automation Robotics Vision (ICARCV), 2010 11th International Conference on},
	year = {2010},
	month = {2010/12//},
	pages = {1 - 6},
	abstract = {In recent years, the theories of Sparse Representation (SR) and Compressed Sensing (CS) have emerged as powerful tools for efficiently processing data in non-traditional ways. An area of promise for these theories is biome $\#$x0301;trie identification. In this paper, we review the role of sparse representation and CS for efficient biome $\#$x0301;trie identification. Algorithms to perform identification from face and iris data are reviewed. By applying Random Projections it is possible to purposively hide the biome $\#$x0301;trie data within a template. This procedure can be effectively employed for securing and protecting personal biome $\#$x0301;trie data against theft. Some of the most compelling challenges and issues that confront research in biometrics using sparse representations and CS are also addressed.},
	keywords = {Biometric identification, Cancelable Biometrics, Compressed sensing, face data, face recognition, iris data, iris recognition, personal biometric data, Random Projections, robust biometrics, sparse representations},
	doi = {10.1109/ICARCV.2010.5707955},
	author = {Patel, Vishal M. and Chellapa, Rama and Tistarelli,M.}
}
