The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper proposes a novel spontaneous facial expression classification method using the facial motion magnification which transforms the subtle facial expressions into the corresponding exaggerated facial expressions. Facial motion magnification consists of four steps: First, we perform the active appearance model (AAM) fitting to extract 70 facial feature points in the face image sequence. Second,...
This thesis aims to develop a system for multiple objects tracking and joint attention between a human and a robot. We propose a new method (modified Multi-CAMSHIFT, MMC), which is based on the characteristics of color and shape probability distribution, to solve the tracking problems for multiple objects. The color information is calculated by MMC improved from CAMSHIFT theory. The shape information...
In spatial face detection stage, in order to eliminate the influence of luminance, a two-dimension Gaussian distribution function, based on the chrominance plane of YCbCr color space, is constructed, then the moving skin area is determined by the luminance component Y, the moving face is detected by support vector machine classifier. In face temporal tracking stage, the eigeface similarity measurement...
A multiple faces tracking system was presented based on Relevance Vector Machine (RVM) and Boosting learning. In this system, a face detector based on Boosting learning is used to detect faces at the first frame, and the face motion model and color model are created. In the tracking process different tracking methods are used according to different states of faces, and the states are changed according...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.