Hands-free vision-based interface for computer accessibility

"Hands-free vision-based interface forcomputer accessibility",
Javier Varona, Cristina Manresa-Yee, Francisco J. Perales, JNCA08'

In order to draw disabled people to new technology, the paper presents a hands-free vision-based interface. First, tracking the facial feature in few frames to initialize the model. No special lighting or static background is important, any orientation must be avoided during initialization. They select nose and eye zone for tracking by color distribution. The symmetry of nose feature points may affect the tracking precision. Locating user's eyes, they focus on eyes and eyebrows by color. Similarly, wearing glasses may result in certain lighting condition and cause error. They apply a weighting function according to the distance between pixels and the eye center. Then, using mean-shift algorithm to tracking. A linear regression method is used to smooth the positions. When recognizing facial gesture, wink recognition is taken into consideration. If the (vertical) iris contours are detected in the image, the eye will be considered as open, otherwise, close.

There may be two different forms to replace the mouse for hands-free computer accessibility. One is directly mapping the nose position onto the screen. Another uses relative head motion which has a predictable tendency and is not as sensitive to the racking accuracy.

More head and facial gestures are planned for improving the system.

Support vector learning for ordinal regression

"Support vector learning for ordinal regression," R. Herbrich, ICANN, 1999

The paper presents a method to solve ordinal regression by support vector. They reformulate the ranking problem into binary classification problem. That is, given the pairs of instances, output their relative ranking according to their classified labels. The idea about applying SVM on ranking is really impressive. However, because the learning process is based on pairs of objects, it may be time-consuming.

The structure and function of complex networks

"The structure and function of complex networks," Newman, 2003.

Generally, there are some terms with regard to a graph, such as vertex, edge, directed/undirected, degree, component, geodesic path, and diameter. Besides, there are also some kinds of networks in the real world. For example, social networks is groups of people with some pattern of interactions between them. The information networks is the network of citations between academic papers or the World Wide Web.
One of the properties of networks is the small-world effect. The effect shows that most pairs of vertices in most networks seem to be connected by a short path through the network, and the information may spread very fast in few steps. Another property is network resilience. Networks vary when vertices are removed or added. That is, the typical path of some paths may increase and the communication between some pairs may become impossible if vertices are removed from the network.
A random graph consists of vertices and the edges with probability. The assigned probabilities are the major study of many papers.