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.
We have applied Latent Topic Models to facial expression recognition. We showed that the latent topic learned from a topic model is very similar to the Action Units defined by psychologists in the Facial Action Coding Systems (FACS). Furthermore, we noted that the topics thus obtained may be correlated with each other, and we tried to model this by the correlated topic model (CTM). Preliminary results...
Support Vector Machines (SVM) is a statistical classification approach which has been successfully applied to various types of problems. However, it has remained largely unexplored for Arabic recognition. SVMs are originally designed for binary classification problems. For multi-class problems, several methods used a combination of binary SVMs while some others solved the problem in one step. This...
Automatic facial action unit (AU) detection is a challenging research topic in computer vision and pattern recognition. Most of the existing approaches design classifiers to detect AUs individually without considering their intrinsic relations. This paper proposes a novel framework to jointly learn the classifiers for detecting the presence and absence of multiple AUs. In our method, hierarchical...
The hierarchical classification method based on SVM multi learning process has proved its abilities to classify large databases. However, some misclassification can be detected through the hierarchy levels. To minimize these errors and improving the rate of classification, we propose to use Nearest Cluster centers (NCC) in the hierarchy structure to correct the classification ambiguities. Obtained...
In this paper we investigate the use of Multitask Learning (MTL) methods to model the commonalities and variations across a set of facial action units (AUs) and also learn the classifiers for detection of multiple AUs simultaneously by exploiting their inner-relations. We studied three variants of MTL algorithms, the Regularized MTL (RMTL), the Multitask Feature Learning (MTFL) and the Alternating...
Automatic recognition of printed mathematical symbols is a fundamental problem for recognition of mathematical expressions. Several classification techniques has been previously used, but there are very few works that compare different classification techniques on the same database and with the same experimental conditions. In this work we have tested classical and novelty classification techniques...
This paper focuses on audio-visual (using facial expression, shoulder and audio cues) classification of spontaneous affect, utilising generative models for classification (i) in terms of Maximum Likelihood Classification with the assumption that the generative model structure in the classifier is correct, and (ii) Likelihood Space Classification with the assumption that the generative model structure...
In this paper, we describe an application of speaker verification using Romanian vowels as speaker's models in case of a small Romanian language database. Vowels models are obtained with continuous HMMs using re-training of the vowels models for every speaker. Afterwards the models are classified with the powerful technique named SVM.
This paper exploits the fact that when GMM and SVM classifiers with roughly the same level of performance exhibit uncorrelated errors they can be combined to produce a better classifier. The gain accrues from combining the descriptive strength of GMM models with the discriminative power of SVM classifiers. This idea, first exploited in the context of speaker recognition [1, 2], is applied to speech...
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.