Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisakis model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: Linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers error rates and then to evaluate the information expressed by the classifiers confusion matrices. © Springer Science+Business Media, LLC 2011

Deep Learning-Based Approach for Missing Data Imputation

The missing values in the datasets are a problem that will decrease the machine learning performance. New methods arerecommended every day to overcome this problem. The methods of statistical, machine learning, evolutionary and deeplearning are among these methods. Although deep learning methods is one of the popular subjects of today, there are limitedstudies in the missing data imputation. Several deep learning techniques have been used to handling missing data, one of themis the autoencoder and its denoising and stacked variants. In this study, the missing value in three different real-world datasetswas estimated by using denoising autoencoder (DAE), k-nearest neighbor (kNN) and multivariate imputation by chainedequations (MICE) methods. The estimation success of the methods was compared according to the root mean square error(RMSE) criterion. It was observed that the DAE method was more successful than other statistical methods in estimating themissing values for large datasets

Machine Analysis of Facial Expressions

No abstract

Editing faces in videos

Editing faces in movies is of interest in the special effects industry. We aim at producing effects such as the addition of accessories interacting correctly with the face or replacing the face of a stuntman with the face of the main actor. The system introduced in this thesis is based on a 3D generative face model. Using a 3D model makes it possible to edit the face in the semantic space of pose, expression, and identity instead of pixel space, and due to its 3D nature allows a modelling of the light interaction. In our system we first reconstruct the 3D face, which is deforming because of expressions and speech, the lighting, and the camera in all frames of a monocular input video. The face is then edited by substituting expressions or identities with those of another video sequence or by adding virtual objects into the scene. The manipulated 3D scene is rendered back into the original video, correctly simulating the interaction of the light with the deformed face and virtual objects. We describe all steps necessary to build and apply the system. This includes registration of training faces to learn a generative face model, semi-automatic annotation of the input video, fitting of the face model to the input video, editing of the fit, and rendering of the resulting scene. While describing the application we introduce a host of new methods, each of which is of interest on its own. We start with a new method to register 3D face scans to use as training data for the face model. For video preprocessing a new interest point tracking and 2D Active Appearance Model fitting technique is proposed. For robust fitting we introduce background modelling, model-based stereo techniques, and a more accurate light model