Biometric recognition has attracted the attention of scientists, investors, government agencies as well as the media for the great potential in many application domains. Among the many developed techniques for biometric recognition, face analysis seems to be the most promising and interesting modality. This lecture will focus on the current state of the art in face recognition technology and its perspectives. The human visual system certainly provides a remarkable benchmark for face recognition, but also an inspiration for algorithmic design. The ability of the human visual system of analysing unknown faces, under different poses and to extract different personal features, is an example of the amount of information which can be extracted from face images. This is not limited to the space or spectral domain, but heavily involves the time evolution of the visual signal. Nonetheless, there are still many open problems which need to be "faced" as well. This not only requires to devise new algorithms but to determine the real potential and limitations of existing techniques, also exploiting the time dimension to boost recognition performances.
This lecture will review several methods for face analysis, based on diverse similarity measure and image representations, both in 2D and 3D. Some new methods are described, tested with conventional and also new databases from real environments. [Go to the full record in the library's catalogue]
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