2 research outputs found
Recognizing Combinations of Facial Action Units with Different Intensity Using a Mixture of Hidden Markov Models and Neural Network
Facial Action Coding System consists of 44 action units (AUs) and more than
7000 combinations. Hidden Markov models (HMMs) classifier has been used
successfully to recognize facial action units (AUs) and expressions due to its
ability to deal with AU dynamics. However, a separate HMM is necessary for each
single AU and each AU combination. Since combinations of AU numbering in
thousands, a more efficient method will be needed. In this paper an accurate
real-time sequence-based system for representation and recognition of facial
AUs is presented. Our system has the following characteristics: 1) employing a
mixture of HMMs and neural network, we develop a novel accurate classifier,
which can deal with AU dynamics, recognize subtle changes, and it is also
robust to intensity variations, 2) although we use an HMM for each single AU
only, by employing a neural network we can recognize each single and
combination AU, and 3) using both geometric and appearance-based features, and
applying efficient dimension reduction techniques, our system is robust to
illumination changes and it can represent the temporal information involved in
formation of the facial expressions. Extensive experiments on Cohn-Kanade
database show the superiority of the proposed method, in comparison with other
classifiers. Keywords: classifier design and evaluation, data fusion, facial
action units (AUs), hidden Markov models (HMMs), neural network (NN)
An Objective Evaluation Metric for image fusion based on Del Operator
In this paper, a novel objective evaluation metric for image fusion is
presented. Remarkable and attractive points of the proposed metric are that it
has no parameter, the result is probability in the range of [0, 1] and it is
free from illumination dependence. This metric is easy to implement and the
result is computed in four steps: (1) Smoothing the images using Gaussian
filter. (2) Transforming images to a vector field using Del operator. (3)
Computing the normal distribution function ({\mu},{\sigma}) for each
corresponding pixel, and converting to the standard normal distribution
function. (4) Computing the probability of being well-behaved fusion method as
the result. To judge the quality of the proposed metric, it is compared to
thirteen well-known non-reference objective evaluation metrics, where eight
fusion methods are employed on seven experiments of multimodal medical images.
The experimental results and statistical comparisons show that in contrast to
the previously objective evaluation metrics the proposed one performs better in
terms of both agreeing with human visual perception and evaluating fusion
methods that are not performed at the same level.Comment: 22 pages, 14 Figure