937 research outputs found

    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

    The first Facial Landmark Tracking in-the-Wild Challenge: benchmark and results

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    Detection and tracking of faces in image sequences is among the most well studied problems in the intersection of statistical machine learning and computer vision. Often, tracking and detection methodologies use a rigid representation to describe the facial region 1, hence they can neither capture nor exploit the non-rigid facial deformations, which are crucial for countless of applications (e.g., facial expression analysis, facial motion capture, high-performance face recognition etc.). Usually, the non-rigid deformations are captured by locating and tracking the position of a set of fiducial facial landmarks (e.g., eyes, nose, mouth etc.). Recently, we witnessed a burst of research in automatic facial landmark localisation in static imagery. This is partly attributed to the availability of large amount of annotated data, many of which have been provided by the first facial landmark localisation challenge (also known as 300-W challenge). Even though now well established benchmarks exist for facial landmark localisation in static imagery, to the best of our knowledge, there is no established benchmark for assessing the performance of facial landmark tracking methodologies, containing an adequate number of annotated face videos. In conjunction with ICCV’2015 we run the first competition/challenge on facial landmark tracking in long-term videos. In this paper, we present the first benchmark for long-term facial landmark tracking, containing currently over 110 annotated videos, and we summarise the results of the competition

    Estimating correspondences of deformable objects "in-the-wild"

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    This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordDuring the past few years we have witnessed the development of many methodologies for building and fitting Statistical Deformable Models (SDMs). The construction of accurate SDMs requires careful annotation of images with regards to a consistent set of landmarks. However, the manual annotation of a large amount of images is a tedious, laborious and expensive procedure. Furthermore, for several deformable objects, e.g. human body, it is difficult to define a consistent set of landmarks, and, thus, it becomes impossible to train humans in order to accurately annotate a collection of images. Nevertheless, for the majority of objects, it is possible to extract the shape by object segmentation or even by shape drawing. In this paper, we show for the first time, to the best of our knowledge, that it is possible to construct SDMs by putting object shapes in dense correspondence. Such SDMs can be built with much less effort for a large battery of objects. Additionally, we show that, by sampling the dense model, a part-based SDM can be learned with its parts being in correspondence. We employ our framework to develop SDMs of human arms and legs, which can be used for the segmentation of the outline of the human body, as well as to provide better and more consistent annotations for body joints.Engineering and Physical Sciences Research Council (EPSRC)TekesEuropean Community Horizon 202

    Estimating Correspondences of Deformable Objects “In-the-wild”

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    During the past few years we have witnessed the development of many methodologies for building and fitting Statistical Deformable Models (SDMs). The construction of accurate SDMs requires careful annotation of images with regards to a consistent set of landmarks. However, the manual annotation of a large amount of images is a tedious, laborious and expensive procedure. Furthermore, for several deformable objects, e.g. human body, it is difficult to define a consistent set of landmarks, and, thus, it becomes impossible to train humans in order to accurately annotate a collection of images. Nevertheless, for the majority of objects, it is possible to extract the shape by object segmentation or even by shape drawing. In this paper, we show for the first time, to the best of our knowledge, that it is possible to construct SDMs by putting object shapes in dense correspondence. Such SDMs can be built with much less effort for a large battery of objects. Additionally, we show that, by sampling the dense model, a part-based SDM can be learned with its parts being in correspondence. We employ our framework to develop SDMs of human arms and legs, which can be used for the segmentation of the outline of the human body, as well as to provide better and more consistent annotations for body joints

    Multi-View Face Recognition From Single RGBD Models of the Faces

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    This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks
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