@conference {12556,
	title = {Multi-biometric cohort analysis for biometric fusion},
	booktitle = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on},
	year = {2008},
	month = {2008/04/31/4},
	pages = {5224 - 5227},
	abstract = {Biometric matching decisions have traditionally been made based solely on a score that represents the similarity of the query biometric to the enrolled biometric(s) of the claimed identity. Fusion schemes have been proposed to benefit from the availability of multiple biometric samples (e.g., multiple samples of the same fingerprint) or multiple different biometrics (e.g., face and fingerprint). These commonly adopted fusion approaches rarely make use of the large number of non-matching biometric samples available in the database in the form of other enrolled identities or training data. In this paper, we study the impact of combining this information with the existing fusion methodologies in a cohort analysis framework. Experimental results are provided to show the usefulness of such a cohort-based fusion of face and fingerprint biometrics.},
	keywords = {(access, analysis;biometrics, biometric, biometrics;fingerprint, biometrics;multi-biometric, cohort, control);face, data;, decisions;face, fusion;biometric, identification;security, MATCHING, of, recognition;fingerprint},
	doi = {10.1109/ICASSP.2008.4518837},
	author = {Aggarwal,G. and Ratha,N. K and Bolle, R.M. and Chellapa, Rama}
}
@conference {12578,
	title = {Video Biometrics},
	booktitle = {Image Analysis and Processing, 2007. ICIAP 2007. 14th International Conference on},
	year = {2007},
	month = {2007/09//},
	pages = {363 - 370},
	abstract = {A strong requirement to come up with secure and user- friendly ways to authenticate and identify people, to safeguard their rights and interests, has probably been the main guiding force behind biometrics research. Though a vast amount of research has been done to recognize humans based on still images, the problem is still far from solved for unconstrained scenarios. This has led to an increased interest in using video for the task of biometric recognition. Not only does video provide more information, but also is more suitable for recognizing humans in general surveillance scenarios. Other than the multitude of still frames, video makes it possible to characterize biometrics based on inherent dynamics like gait which is not possible with still images. In this paper, we describe several recent algorithms to illustrate the usefulness of videos to identify humans. A brief discussion on remaining challenges is also included.},
	keywords = {(access, analysis;video, biometrics;biometrics, control);face, dynamics;ofbiometric, images;surveillance, inherent, MOTION, processing;, recognition;image, recognition;still, scenarios;unconstrained, scenarios;video, signal},
	doi = {10.1109/ICIAP.2007.4362805},
	author = {Chellapa, Rama and Aggarwal,G.}
}
