2,652 research outputs found
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
Automatic facial analysis for objective assessment of facial paralysis
Facial Paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann Scale. Experiments show the Radial Basis Function (RBF) Neural Network to have superior performance
Hand2Face: Automatic Synthesis and Recognition of Hand Over Face Occlusions
A person's face discloses important information about their affective state.
Although there has been extensive research on recognition of facial
expressions, the performance of existing approaches is challenged by facial
occlusions. Facial occlusions are often treated as noise and discarded in
recognition of affective states. However, hand over face occlusions can provide
additional information for recognition of some affective states such as
curiosity, frustration and boredom. One of the reasons that this problem has
not gained attention is the lack of naturalistic occluded faces that contain
hand over face occlusions as well as other types of occlusions. Traditional
approaches for obtaining affective data are time demanding and expensive, which
limits researchers in affective computing to work on small datasets. This
limitation affects the generalizability of models and deprives researchers from
taking advantage of recent advances in deep learning that have shown great
success in many fields but require large volumes of data. In this paper, we
first introduce a novel framework for synthesizing naturalistic facial
occlusions from an initial dataset of non-occluded faces and separate images of
hands, reducing the costly process of data collection and annotation. We then
propose a model for facial occlusion type recognition to differentiate between
hand over face occlusions and other types of occlusions such as scarves, hair,
glasses and objects. Finally, we present a model to localize hand over face
occlusions and identify the occluded regions of the face.Comment: Accepted to International Conference on Affective Computing and
Intelligent Interaction (ACII), 201
Machine Analysis of Facial Expressions
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