14,466 research outputs found
Machine Analysis of Facial Expressions
No abstract
Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression
We present techniques for improving performance driven facial animation,
emotion recognition, and facial key-point or landmark prediction using learned
identity invariant representations. Established approaches to these problems
can work well if sufficient examples and labels for a particular identity are
available and factors of variation are highly controlled. However, labeled
examples of facial expressions, emotions and key-points for new individuals are
difficult and costly to obtain. In this paper we improve the ability of
techniques to generalize to new and unseen individuals by explicitly modeling
previously seen variations related to identity and expression. We use a
weakly-supervised approach in which identity labels are used to learn the
different factors of variation linked to identity separately from factors
related to expression. We show how probabilistic modeling of these sources of
variation allows one to learn identity-invariant representations for
expressions which can then be used to identity-normalize various procedures for
facial expression analysis and animation control. We also show how to extend
the widely used techniques of active appearance models and constrained local
models through replacing the underlying point distribution models which are
typically constructed using principal component analysis with
identity-expression factorized representations. We present a wide variety of
experiments in which we consistently improve performance on emotion
recognition, markerless performance-driven facial animation and facial
key-point tracking.Comment: to appear in Image and Vision Computing Journal (IMAVIS
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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