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 new method for comparing 3D facial shapes using facial level curves. The pair- and segment-wise distances between the level curves comprise the spatio-temporal features for expression recognition from 3D dynamic faces. The paper further introduces universal background modeling and maximum a posteriori adaptation for hidden Markov models, leading to a decision boundary focus classification...
Automatic facial expression recognition from non-frontal views is a challenging research topic which has recently started to attract the attention of the research community. In this paper, we propose a novel approach to tackling this problem based on the ergodic hidden Markov model (EHMM) supervector representation of facial images. First, the scale-invariant feature transform (SIFT) feature vectors...
Structured Support Vector Machine (SVM) is a recently developed extension of the very successful SVM approach, which can efficiently classify structured pattern with maximized margin. This paper presents an initial attempt for phoneme recognition using structured SVM. We simply learn the basic framework of HMMs in configuring the structured SVM. In the preliminary experiments with TIMIT corpus, the...
One important class of state emission densities of the hiddenMarkov model (HMM) is the Gaussian mixture densities. The classical Baum-Welch algorithm often fails to reliably learn the Gaussian mixture densities when there is insufficient training data, due to the large number of free parameters present in the model. In this paper, we propose a novel strategy for robustly and accurately learning the...
In this letter, we propose a novel vector representation of stochastic signals for pattern recognition (PR) based on adapted ergodic hidden Markov models (HMMs). This vector representation is generic in nature and may be used with various types of stochastic signals (e.g., image, speech, etc.) and applied to a broad range of PR tasks (e.g., classification, regression, etc.). More importantly, by combining...
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.