1,713 research outputs found

    The computer nose best

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    LBP-based periocular recognition on challenging face datasets

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    LBP-based periocular recognition on challenging face datasets

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    This work develops a novel face-based matcher composed of a multi-resolution hierarchy of patch-based feature descriptors for periocular recognition - recognition based on the soft tissue surrounding the eye orbit. The novel patch-based framework for periocular recognition is compared against other feature descriptors and a commercial full-face recognition system against a set of four uniquely challenging face corpora. The framework, hierarchical three-patch local binary pattern, is compared against the three-patch local binary pattern and the uniform local binary pattern on the soft tissue area around the eye orbit. Each challenge set was chosen for its particular non-ideal face representations that may be summarized as matching against pose, illumination, expression, aging, and occlusions. The MORPH corpora consists of two mug shot datasets labeled Album 1 and Album 2. The Album 1 corpus is the more challenging of the two due to its incorporation of print photographs (legacy) captured with a variety of cameras from the late 1960s to 1990s. The second challenge dataset is the FRGC still image set. Corpus three, Georgia Tech face database, is a small corpus but one that contains faces under pose, illumination, expression, and eye region occlusions. The final challenge dataset chosen is the Notre Dame Twins database, which is comprised of 100 sets of identical twins and 1 set of triplets. The proposed framework reports top periocular performance against each dataset, as measured by rank-1 accuracy: (1) MORPH Album 1, 33.2%; (2) FRGC, 97.51%; (3) Georgia Tech, 92.4%; and (4) Notre Dame Twins, 98.03%. Furthermore, this work shows that the proposed periocular matcher (using only a small section of the face, about the eyes) compares favorably to a commercial full-face matcher

    A review of age estimation research to evaluate its inclusion in automated child pornography detection

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    The uses of artificial intelligence (AI) today seem limitless. It has helped organisations understand their customers more, provide them with better, more tailored services, and helped people with disabilities understand the world they previously could not. There are also many areas of current research for the use of AI. Aiding law-enforcement when they must analyse evidence of an indecent nature is one example where the use of AI, if successful, could enhance detection of indecent images and also reduce the workload and stress on the law enforcement staff employed in such activities. Working with indecent images of minors is particularly stressful. This paper reviews the current stage at which artificial intelligence finds itself when estimating a person’s age. By reviewing its accuracy, it is possible to evaluate the feasibility of its inclusion in an artificial-intelligence-aided evidence analysis tool. With artificial intelligence currently capable of estimating a person’s age to within a few years, its incorporation would most certainly allow photographs to be analysed and flagged if anyone is suspected of being underage
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