1,249 research outputs found
Signal processing and machine learning techniques for automatic image-based facial expression recognition
PhD ThesisIn this thesis novel signal processing and machine learning techniques are
proposed and evaluated for automatic image-based facial expression recognition,
which are aimed to progress towards real world operation.
A thorough evaluation of the performance of certain image-based expression
recognition techniques is performed using a posed database and for the rst time
three progressively more challenging spontaneous databases. These methods
exploit the principles of sparse representation theory with identity-independent
expression recognition using di erence images.
The second contribution exploits a low complexity method to extract geometric
features from facial expression images. The misalignment problem of the training
images is solved and the performance of both geometric and appearance features
is assessed on the same three spontaneous databases. A deep network framework
that contains auto-encoders is used to form an improved classi er.
The nal work focuses upon enhancing the expression recognition performance by
the selection and fusion of di erent types of features comprising geometric
features and two sorts of appearance features. This provides a rich feature vector
by which the best representation of the spontaneous facial features is obtained.
Subsequently, the computational complexity is reduced by maintaining important
location information by concentrating on the crucial roles of the facial regions as
the basic processing instead of the entire face, where the local binary patterns and
local phase quantization features are extracted automatically by means of
detecting two important regions of the face. Next, an automatic method for
splitting the training e ort of the initial network into several networks and
multi-classi ers namely a surface network and bottom network are used to solve
the problem and to enhance the performance.
All methods are evaluated in a MATLAB framework and confusion matrices and
average facial expression recognition accuracy are used as the performance
metrics.Ministry of Higher Education
and Scienti c Research in Iraq (MOHESR
Facial Expression Recognition from World Wild Web
Recognizing facial expression in a wild setting has remained a challenging
task in computer vision. The World Wide Web is a good source of facial images
which most of them are captured in uncontrolled conditions. In fact, the
Internet is a Word Wild Web of facial images with expressions. This paper
presents the results of a new study on collecting, annotating, and analyzing
wild facial expressions from the web. Three search engines were queried using
1250 emotion related keywords in six different languages and the retrieved
images were mapped by two annotators to six basic expressions and neutral. Deep
neural networks and noise modeling were used in three different training
scenarios to find how accurately facial expressions can be recognized when
trained on noisy images collected from the web using query terms (e.g. happy
face, laughing man, etc)? The results of our experiments show that deep neural
networks can recognize wild facial expressions with an accuracy of 82.12%
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