@article {18197,
	title = {Digital image forensics via intrinsic fingerprints},
	journal = {Information Forensics and Security, IEEE Transactions on},
	volume = {3},
	year = {2008},
	month = {2008/03//},
	pages = {101 - 117},
	abstract = {Digital imaging has experienced tremendous growth in recent decades, and digital camera images have been used in a growing number of applications. With such increasing popularity and the availability of low-cost image editing software, the integrity of digital image content can no longer be taken for granted. This paper introduces a new methodology for the forensic analysis of digital camera images. The proposed method is based on the observation that many processing operations, both inside and outside acquisition devices, leave distinct intrinsic traces on digital images, and these intrinsic fingerprints can be identified and employed to verify the integrity of digital data. The intrinsic fingerprints of the various in-camera processing operations can be estimated through a detailed imaging model and its component analysis. Further processing applied to the camera captured image is modelled as a manipulation filter, for which a blind deconvolution technique is applied to obtain a linear time-invariant approximation and to estimate the intrinsic fingerprints associated with these postcamera operations. The absence of camera-imposed fingerprints from a test image indicates that the test image is not a camera output and is possibly generated by other image production processes. Any change or inconsistencies among the estimated camera-imposed fingerprints, or the presence of new types of fingerprints suggest that the image has undergone some kind of processing after the initial capture, such as tampering or steganographic embedding. Through analysis and extensive experimental studies, this paper demonstrates the effectiveness of the proposed framework for nonintrusive digital image forensics.},
	keywords = {ACQUISITION, analysis;intrinsic, approximation;cameras;digital, camera, deconvolution;digital, devices;blind, fingerprints;time, forensics;forensic, identification;image, image, images;digital, invariant, photography;fingerprint, sensors;},
	isbn = {1556-6013},
	doi = {10.1109/TIFS.2007.916010},
	author = {Swaminathan,A. and M. Wu and Liu,K. J.R}
}
@conference {12594,
	title = {Joint Acoustic-Video Fingerprinting of Vehicles, Part II},
	booktitle = {Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on},
	volume = {2},
	year = {2007},
	month = {2007/04//},
	pages = {II-749 -II-752 - II-749 -II-752},
	abstract = {In this second paper, we first show how to estimate the wheelbase length of a vehicle using line metrology in video. We then address the vehicle fingerprinting problem using vehicle silhouettes and color invariants. We combine the acoustic metrology and classification results discussed in Part I with the video results to improve estimation performance and robustness. The acoustic video fusion is achieved in a Bayesian framework by assuming conditional independence of the observations of each modality. For the metrology density functions, Laplacian approximations are used for computational efficiency. Experimental results are given using field data},
	keywords = {acoustic-video, analysis;video, approximations;acoustic, Bayesian, colour, density, efficiency;joint, estimation;Bayes, fingerprinting;metrology, framework;Laplacian, functions;performance, fusion;color, identification;image, invariants;computational, methods;acoustic, metrology;acoustic, processing;, processing;fingerprint, signal, video},
	doi = {10.1109/ICASSP.2007.366344},
	author = {Cevher, V. and Guo, F. and Sankaranarayanan,A. C and Chellapa, Rama}
}
@conference {13165,
	title = {Estimation of Anthropomeasures from a Single Calibrated Camera},
	booktitle = {Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on},
	year = {2006},
	month = {2006/04//},
	pages = {499 - 504},
	abstract = {We are interested in the recovery of anthropometric dimensions of the human body from calibrated monocular sequences, and their use in multi-target tracking across multiple cameras and identification of individual people. In this paper, we focus on two specific anthropomeasures that are relatively easy to estimate from low-resolution images: stature and shoulder breadth. Precise average estimates are obtained for each anthropomeasure by combining measurements from multiple frames in the sequence. Our contribution is two-fold: (i) a novel technique for automatic and passive estimation of shoulder breadth, that is based on modelling the shoulders as an ellipse, and (U) a novel method for increasing the accuracy of the mean estimates of both anthropomeasures. The latter is based on the observation that major sources of error in the measurements are landmark localization the 2D image and 3D modelling error, and that both of these are correlated with gait phase and body orientation with respect to the camera. Consequently, estimation error can be significantly reduced via appropriate selection or control of these two variables},
	keywords = {anthropomeasures, calibrated, camera;anthropometry;error, estimation;error, estimation;gait, identification;image, phase;image, recovery;image, resolution;image, resolution;monocular, restoration;image, sensors;image, sequences;, sequences;single, statistics;image},
	doi = {10.1109/FGR.2006.37},
	author = {BenAbdelkader,C. and Davis, Larry S.}
}
