58,437 research outputs found

    Face recognition technologies for evidential evaluation of video traces

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    Human recognition from video traces is an important task in forensic investigations and evidence evaluations. Compared with other biometric traits, face is one of the most popularly used modalities for human recognition due to the fact that its collection is non-intrusive and requires less cooperation from the subjects. Moreover, face images taken at a long distance can still provide reasonable resolution, while most biometric modalities, such as iris and fingerprint, do not have this merit. In this chapter, we discuss automatic face recognition technologies for evidential evaluations of video traces. We first introduce the general concepts in both forensic and automatic face recognition , then analyse the difficulties in face recognition from videos . We summarise and categorise the approaches for handling different uncontrollable factors in difficult recognition conditions. Finally we discuss some challenges and trends in face recognition research in both forensics and biometrics . Given its merits tested in many deployed systems and great potential in other emerging applications, considerable research and development efforts are expected to be devoted in face recognition in the near future

    A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"

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    Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second authorshi

    Unsupervised learning of clutter-resistant visual representations from natural videos

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    Populations of neurons in inferotemporal cortex (IT) maintain an explicit code for object identity that also tolerates transformations of object appearance e.g., position, scale, viewing angle [1, 2, 3]. Though the learning rules are not known, recent results [4, 5, 6] suggest the operation of an unsupervised temporal-association-based method e.g., Foldiak's trace rule [7]. Such methods exploit the temporal continuity of the visual world by assuming that visual experience over short timescales will tend to have invariant identity content. Thus, by associating representations of frames from nearby times, a representation that tolerates whatever transformations occurred in the video may be achieved. Many previous studies verified that such rules can work in simple situations without background clutter, but the presence of visual clutter has remained problematic for this approach. Here we show that temporal association based on large class-specific filters (templates) avoids the problem of clutter. Our system learns in an unsupervised way from natural videos gathered from the internet, and is able to perform a difficult unconstrained face recognition task on natural images: Labeled Faces in the Wild [8]
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