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The use of ear information for people identification has been under testing at least for 100 years. However, it is still an open issue if the ears can be considered unique or unique enough to be used as biometric feature. In this paper a biometric system for human identification based on ear recognition is presented. The ear is modeled as a set of contours extracted from the ear image with an edge potential function. The matching algorithm has been tested in presence of several image modifications. Two human ear databases have been used for the tests. The experimental results show the effectiveness of the proposed scheme.

Source identification for digital content is one of the main branches of digital image forensics. It relies on the extraction of the photo-response non-uniformity (PRNU) noise as a unique intrinsic fingerprint that efficiently characterizes the digital device which generated the content. Such noise is estimated as the difference between the content and its de-noised version obtained via denoising filter processing. This paper proposes a performance comparison of different denoising filters for source identification purposes. In particular, results achieved with a sophisticated 3D filter are presented and discussed with respect to state-of-the-art denoising filters previously employed in such a context.