1,205 research outputs found
The Design of a Multimedia-Forensic Analysis Tool (M-FAT)
Digital forensics has become a fundamental
requirement for law enforcement due to the growing
volume of cyber and computer-assisted crime. Whilst
existing commercial tools have traditionally focused
upon string-based analyses (e.g., regular
expressions, keywords), less effort has been placed
towards the development of multimedia-based
analyses. Within the research community, more focus
has been attributed to the analysis of multimedia
content; they tend to focus upon highly specialised
specific scenarios such as tattoo identification,
number plate recognition, suspect face recognition
and manual annotation of images. Given the everincreasing volume of multimedia content, it is
essential that a holistic Multimedia-Forensic
Analysis Tool (M-FAT) is developed to extract, index,
analyse the recovered images and provide an
investigator with an environment with which to ask
more abstract and cognitively challenging questions
of the data. This paper proposes such a system,
focusing upon a combination of object and facial
recognition to provide a robust system. This system
will enable investigators to perform a variety of
forensic analyses that aid in reducing the time, effort
and cognitive load being placed on the investigator to
identify relevant evidence
Discriminative Appearance Models for Face Alignment
The proposed face alignment algorithm uses local gradient features as the appearance representation. These features are obtained by pixel value comparison, which provide robustness against changes in illumination, as well as partial occlusion and local deformation due to the locality. The adopted features are modeled in three discriminative methods, which correspond to different alignment cost functions. The discriminative appearance modeling alleviate the generalization problem to some extent
AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses
Facial landmark localization aims to detect the predefined points of human
faces, and the topic has been rapidly improved with the recent development of
neural network based methods. However, it remains a challenging task when
dealing with faces in unconstrained scenarios, especially with large pose
variations. In this paper, we target the problem of facial landmark
localization across large poses and address this task based on a
split-and-aggregate strategy. To split the search space, we propose a set of
anchor templates as references for regression, which well addresses the large
variations of face poses. Based on the prediction of each anchor template, we
propose to aggregate the results, which can reduce the landmark uncertainty due
to the large poses. Overall, our proposed approach, named AnchorFace, obtains
state-of-the-art results with extremely efficient inference speed on four
challenging benchmarks, i.e. AFLW, 300W, Menpo, and WFLW dataset. Code will be
available at https://github.com/nothingelse92/AnchorFace.Comment: To appear in AAAI 202
Robust Methods for Visual Tracking and Model Alignment
The ubiquitous presence of cameras and camera networks needs the development of robust visual analytics algorithms. As the building block of many video visual surveillance tasks, a robust visual tracking algorithm plays an important role in achieving the goal of automatic and robust surveillance. In practice, it is critical to know when and where the tracking algorithm fails so that remedial measures can be taken to resume tracking. We propose a novel performance evaluation strategy for tracking systems using a time-reversed Markov chain. We also present a novel bidirectional tracker to achieve better robustness. Instead of looking only forward in the time domain, we incorporate both forward and backward processing of video frames using a time-reversibility constraint. When the objects of interest in surveillance applications have relatively stable structures, the parameterized shape model of objects can be usually built or learned from sample images, which allows us to perform more accurate tracking. We present a machine learning method to learn a scoring function without local extrema to guide the gradient descent/accent algorithm and find the optimal parameters of the shape model. These algorithms greatly improve the robustness of video analysis systems in practice
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