14,938 research outputs found
Timing is everything: A spatio-temporal approach to the analysis of facial actions
This thesis presents a fully automatic facial expression analysis system based on the Facial Action
Coding System (FACS). FACS is the best known and the most commonly used system to describe
facial activity in terms of facial muscle actions (i.e., action units, AUs). We will present our research
on the analysis of the morphological, spatio-temporal and behavioural aspects of facial expressions.
In contrast with most other researchers in the field who use appearance based techniques, we use a
geometric feature based approach. We will argue that that approach is more suitable for analysing
facial expression temporal dynamics. Our system is capable of explicitly exploring the temporal
aspects of facial expressions from an input colour video in terms of their onset (start), apex (peak)
and offset (end).
The fully automatic system presented here detects 20 facial points in the first frame and tracks them
throughout the video. From the tracked points we compute geometry-based features which serve as
the input to the remainder of our systems. The AU activation detection system uses GentleBoost
feature selection and a Support Vector Machine (SVM) classifier to find which AUs were present in an
expression. Temporal dynamics of active AUs are recognised by a hybrid GentleBoost-SVM-Hidden
Markov model classifier. The system is capable of analysing 23 out of 27 existing AUs with high
accuracy.
The main contributions of the work presented in this thesis are the following: we have created a
method for fully automatic AU analysis with state-of-the-art recognition results. We have proposed
for the first time a method for recognition of the four temporal phases of an AU. We have build the
largest comprehensive database of facial expressions to date. We also present for the first time in the
literature two studies for automatic distinction between posed and spontaneous expressions
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
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics
Smile is one of the key elements in identifying emotions and present state of
mind of an individual. In this work, we propose a cluster of approaches to
classify posed and spontaneous smiles using deep convolutional neural network
(CNN) face features, local phase quantization (LPQ), dense optical flow and
histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for
micro-expression smile amplification along with three normalization procedures
for distinguishing posed and spontaneous smiles. Although the deep CNN face
model is trained with large number of face images, HOG features outperforms
this model for overall face smile classification task. Using EVM to amplify
micro-expressions did not have a significant impact on classification accuracy,
while the normalizing facial features improved classification accuracy. Unlike
many manual or semi-automatic methodologies, our approach aims to automatically
classify all smiles into either `spontaneous' or `posed' categories, by using
support vector machines (SVM). Experimental results on large UvA-NEMO smile
database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial
Behavior Analysi
Machine Analysis of Facial Expressions
No abstract
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text
overlap with arXiv:1604.0150
Fully automatic facial action unit detection and temporal analysis
In this work we report on the progress of building a system that enables fully automated fast and robust facial expression recognition from face video. We analyse subtle changes in facial expression by recognizing facial muscle action units (AUs) and analysing their temporal behavior. By detecting AUs from face video we enable the analysis of various facial communicative signals including facial expressions of emotion, attitude and mood. For an input video picturing a facial expression we detect per frame whether any of 15 different AUs is activated, whether that facial action is in the onset, apex, or offset phase, and what the total duration of the activation in question is. We base this process upon a set of spatio-temporal features calculated from tracking data for 20 facial fiducial points. To detect these 20 points of interest in the first frame of an input face video, we utilize a fully automatic, facial point localization method that uses individual feature GentleBoost templates built from Gabor wavelet features. Then, we exploit a particle filtering scheme that uses factorized likelihoods and a novel observation model that combines a rigid and a morphological model to track the facial points. The AUs displayed in the input video and their temporal segments are recognized finally by Support Vector Machines trained on a subset of most informative spatio-temporal features selected by AdaBoost. For Cohn-Kanade and MMI databases, the proposed system classifies 15 AUs occurring alone or in combination with other AUs with a mean agreement rate of 90.2 % with human FACS coders
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