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Holistic facial composite systems: are they compatible with witness recall?
Facial composite systems offer a particular challenge to human-computer interaction as they must facilitate several cognitively complex tasks and also aid communication between the operator and the witness. This paper presents the findings from a survey conducted with UK police composite operators that explored some of the issues involved in composite construction. A particular emphasis was placed on the information that witnesses report and its compatibility with both the composite system interface and the underlying construction method used by the system
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PDE Face: A Novel 3D Face Model
YesWe introduce a novel approach to face models, which
exploits the use of Partial Differential Equations (PDE) to
generate the 3D face. This addresses some common
problems of existing face models. The PDE face benefits
from seamless merging of surface patches by using only a
relatively small number of parameters based on boundary
curves. The PDE face also provides users with a great
degree of freedom to individualise the 3D face by
adjusting a set of facial boundary curves. Furthermore, we
introduce a uv-mesh texture mapping method. By
associating the texels of the texture map with the vertices
of the uv mesh in the PDE face, the new texture mapping
method eliminates the 3D-to-2D association routine in
texture mapping. Any specific PDE face can be textured
without the need for the facial expression in the texture
map to match exactly that of the 3D face model
A dynamic texture based approach to recognition of facial actions and their temporal models
In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the dynamics and the appearance in the face region of an input video are compared: an extended version of Motion History Images and a novel method based on Nonrigid Registration using Free-Form Deformations (FFDs). The extracted motion representation is used to derive motion orientation histogram descriptors in both the spatial and temporal domain. Per AU, a combination of discriminative, frame-based GentleBoost ensemble learners and dynamic, generative Hidden Markov Models detects the presence of the AU in question and its temporal segments in an input image sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, the proposed method achieved an average event recognition accuracy of 89.2 percent for the MHI method and 94.3 percent for the FFD method. The generalization performance of the FFD method has been tested using the Cohn-Kanade database. Finally, we also explored the performance on spontaneous expressions in the Sensitive Artificial Listener data set
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