It is commonly accepted that users of a biometric system may have differing degrees of accuracy within the system. Some people may have trouble authenticating, while others may be particularly vulnerable to impersonation. Goats, wolves, and lambs are labels commonly applied to these problem users. These user types are defined in terms of verification performance when users are matched against themselves (goats) or when matched against others (lambs and wolves). The relationship between a user's genuine and impostor match results suggests four new user groups: worms, doves, chameleons, and phantoms. We establish formal definitions for these animals and a statistical test for their existence. A thorough investigation is conducted using a broad range of biometric modalities, including 2D and 3D faces, fingerprints, iris, speech, and keystroke dynamics. Patterns that emerge from the results expose novel, important, and encouraging insights into the nature of biometric match results. A new framework for the evaluation of biometric systems based on the biometric menagerie, as opposed to collective statistics, is proposed.

Semi-transparent watermarks (logos) are a prominent feature of modern television programs. They are employed to label broadcasted programs, but not commercials, and therefore their absence can be exploited to filter out advertisements. A real-time template-based pixel matching algorithm for detecting known logos in video is presented. The logo detector achieves a high degree of precision (99.53%) and recall (97.23%) despite significant spatial variations inherent in the decompressed source video. When combined with other feature detectors, it forms the foundation of a live commercial detection system with applications in video archiving and embedded environments