21,821 research outputs found
Machine Analysis of Facial Expressions
No abstract
3D face tracking and multi-scale, spatio-temporal analysis of linguistically significant facial expressions and head positions in ASL
Essential grammatical information is conveyed in signed languages by clusters of events involving facial expressions and movements of the head and upper body. This poses a significant challenge for computer-based sign language recognition. Here, we present new methods for the recognition of nonmanual grammatical markers in American Sign Language (ASL) based on: (1) new 3D tracking methods for the estimation of 3D head pose and facial expressions to determine the relevant low-level features; (2) methods for higher-level analysis of component events (raised/lowered eyebrows, periodic head nods and head shakes) used in grammatical markings—with differentiation of temporal phases (onset, core, offset, where appropriate), analysis of their characteristic properties, and extraction of corresponding features; (3) a 2-level learning framework to combine lowand high-level features of differing spatio-temporal scales. This new approach achieves significantly better tracking and recognition results than our previous methods
Computer-based tracking, analysis, and visualization of linguistically significant nonmanual events in American Sign Language (ASL)
Our linguistically annotated American Sign Language (ASL) corpora have formed a basis for research to automate detection by
computer of essential linguistic information conveyed through facial expressions and head movements. We have tracked head position
and facial deformations, and used computational learning to discern specific grammatical markings. Our ability to detect, identify, and
temporally localize the occurrence of such markings in ASL videos has recently been improved by incorporation of (1) new techniques
for deformable model-based 3D tracking of head position and facial expressions, which provide significantly better tracking accuracy
and recover quickly from temporary loss of track due to occlusion; and (2) a computational learning approach incorporating 2-level
Conditional Random Fields (CRFs), suited to the multi-scale spatio-temporal characteristics of the data, which analyses not only
low-level appearance characteristics, but also the patterns that enable identification of significant gestural components, such as
periodic head movements and raised or lowered eyebrows. Here we summarize our linguistically motivated computational approach
and the results for detection and recognition of nonmanual grammatical markings; demonstrate our data visualizations, and discuss the
relevance for linguistic research; and describe work underway to enable such visualizations to be produced over large corpora and
shared publicly on the Web
Head Tracking via Robust Registration in Texture Map Images
A novel method for 3D head tracking in the presence of large head rotations and facial expression changes is described. Tracking is formulated in terms of color image registration in the texture map of a 3D surface model. Model appearance is recursively updated via image mosaicking in the texture map as the head orientation varies. The resulting dynamic texture map provides a stabilized view of the face that can be used as input to many existing 2D techniques for face recognition, facial expressions analysis, lip reading, and eye tracking. Parameters are estimated via a robust minimization procedure; this provides robustness to occlusions, wrinkles, shadows, and specular highlights. The system was tested on a variety of sequences taken with low quality, uncalibrated video cameras. Experimental results are reported
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