3 research outputs found
Real-time Automatic Emotion Recognition from Body Gestures
Although psychological research indicates that bodily expressions convey
important affective information, to date research in emotion recognition
focused mainly on facial expression or voice analysis. In this paper we propose
an approach to realtime automatic emotion recognition from body movements. A
set of postural, kinematic, and geometrical features are extracted from
sequences 3D skeletons and fed to a multi-class SVM classifier. The proposed
method has been assessed on data acquired through two different systems: a
professionalgrade optical motion capture system, and Microsoft Kinect. The
system has been assessed on a "six emotions" recognition problem, and using a
leave-one-subject-out cross validation strategy, reached an overall recognition
rate of 61.3% which is very close to the recognition rate of 61.9% obtained by
human observers. To provide further testing of the system, two games were
developed, where one or two users have to interact to understand and express
emotions with their body
COMBINED BINARY CLASSIFIERS WITH APPLICATIONS TO SPEECH RECOGNITION
Many applications require classification of examples into one of several classes. A common way of designing such classifiers is to determine the class based on the outputs of several binary classifiers. We consider some of the most popular methods for combining the decisions of the binary classifiers, and improve existing bounds on the error rates of the combined classifier over the training set. We also describe a new method for combining binary classifiers. The method is based on stacking a neural network and, when used with support vector machines as the binary learners, substantially decreased the error rate in two vowel classification tasks. 1
Combined Binary Classifiers With Applications To Speech Recognition
Many applications require classification of examples into one of several classes. A common way of designing such classifiers is to determine the class based on the outputs of several binary classifiers. We consider some of the most popular methods for combining the decisions of the binary classifiers, and improve existing bounds on the error rates of the combined classifier over the training set. We also describe a new method for combining binary classifiers. The method is based on stacking a neural network and, when used with support vector machines as the binary learners, substantially decreased the error rate in two vowel classification tasks