Learner’s Emotional state has an important impact on affective and cognitive processes for classroom teaching. It is quite necessary to detect learner’s emotion state unconsciously in teaching and learning processes. A fast facial expression recognition algorithm is presented to detect the emotional state of the learner in real learning environment. Gabor convolutional network (GCN) is used to classify the facial expression. The image extracted from teaching and learning environment need to be preprocessed for accelerating the expression recognition. A skin color segmentation model, generalized Gaussian mixture distribution (GGMD), is designed by using expectation and maximization (EM) algorithm to detect the facial area rapidly. Then a fast facial expression recognition algorithm is designed by using the skin color model and the GCN. Experiment results show the satisfied accuracy and excellent time performance of the system.
Some researches show that learner’s Emotional state has an important impact on affective and cognitive processes influencing learning. A positive emotional state can enhance learning outcome. It is important to detect learner’s emotion state in learning processes unconsciously. Generally, emotions can be classified within the two dimensions, valence and activation. Happiness is an activating positive valence emotional state. This paper presents a happiness emotion detection method based on deep learning. Firstly, a certain amount of face images which include static emotion are selected from the image database. Faces are detected by using a face detector and aligned by using eye locations. Then, the face images are clipped into proper size to match the convolutional neural network input. In our classifier, input layer accepts single channel to process grayscale images, and the output layer outputs two classes, i.e. happiness emotion and non-happiness. Fourfold cross-validation is performed on the facial expression image dataset which is divided into four subsets randomly. In every round of cross validation, one subset is used for testing and other three subsets are used for training. The experiment results show that the average accuracy is up to 98.78 percent which is enough to use in learning outcome evaluation.
It is important to accurately fit the unknown probability density functions of background or object. To solve this problem,
the Burr distribution is introduced. Three-parameter Burr distribution can cover a wide range of distribution. The
expectation maximization algorithm is used to deal with the estimation difficulty in the Burr distribution model. The
expectation maximization algorithm starts from a set of selected appropriate parameters’ initial values, and then iterates
the expectation-step and maximization-step until convergence to produce result parameters. The experiment results show
that the Burr distribution could depicts quite successfully the probability density function of a significant class of image,
and comparatively the method has low computing complexity.
Time complexity is one of the biggest problems for fractal image compression algorithm which can bring about high
compression ratio. However, there is inherently data parallelism for fractal image compression algorithm. Naturally,
parallel computation scheme would be used to deal with it. This paper uses "equal division load" balancing algorithm to
design parallel fractal coding algorithm and implement the fractal image compression. "Equal division load" balancing
algorithm distributes computation tasks to all processors equally. Load in every node is divided into smaller tasks based
on all power of nodes on network, and then these smaller tasks are sent to corresponding nodes to balance the load
among nodes. Analysis shows that the algorithm greatly reduces the component task execution time.
The Guassian distribution model is often used to characterize the statistical behavior of image or other multimedia signal,
and applied in fitting probability density functions of a signal. But, in practically, the probability density function of data
source may be inherently non-Gaussian. As the distribution family covers most of the common distribution types and the
frequency curves provided by the family are as wide as in general use, this paper considers Johnson distribution family to
estimate the unknown parameters and approximate the empirical distribution. The method uses the moments to initialize
the parameters of the distribution family, and then calculates parameters by using EM algorithm. The experiment results
show that the fitted model could depicts quite successfully the both Gaussian and non-Gaussian probability density
function of image intensity, and comparatively the method has low computing complexity.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.