28885654
Multimodal human emotion/expression recognition
Recognizing human facial expression and emotion by computer is an interesting and challenging problem. Many have investigated emotional contents in speech alone, or recognition of human facial expressions solely from images. However, relatively little has been done in combining these two modalities for recognizing human emotions. L.C. De Silva et al. (1997) studied human subjects' ability to recognize emotions from viewing video clips of facial expressions and listening to the corresponding emotional speech stimuli. They found that humans recognize some emotions better by audio information, and other emotions better by video. They also proposed an algorithm to integrate both kinds of inputs to mimic human's recognition process. While attempting to implement the algorithm, we encountered difficulties which led us to a different approach. We found these two modalities to be complimentary. By using both, we show it is possible to achieve higher recognition rates than either modality alone.
33621532
Emotional expressions in audiovisual human computer interaction
Visual and auditory modalities are two of the most commonly used media in interactions between humans. The authors describe a system to continuously monitor the user's voice and facial motions for recognizing emotional expressions. Such an ability is crucial for intelligent computers that take on a social role such as an actor or a companion. We outline methods to extract audio and visual features useful for classifying emotions. Audio and visual information must be handled appropriately in single-modal and bimodal situations. We report audio-only and video-only emotion recognition on the same subjects, in person-dependent and person-independent fashions, and outline methods to handle bimodal recognition.
10243856
A new LDA-based face recognition system which can solve the small sample size problem
A new LDA-based face recognition system is presented in this paper. Linear discriminant analysis (LDA) is one of the most popular linear projection techniques for feature extraction. The major drawback of applying LDA is that it may encounter the small sample size problem. In this paper, we propose a new LDA-based technique which can solve the small sample size problem. We also prove that the most expressive vectors derived in the null space of the within-class scatter matrix using principal component analysis (PCA) are equal to the optimal discriminant vectors derived in the original space using LDA. The experimental results show that the new LDA process improves the performance of a face recognition system signi"cantly. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
7065561
Partitioning Graphs Into Hamiltonian Ones
Abstract In this paper, we present an (optimal) linear-time algorithm that decides whether a bipartite permutation graph can be partitioned into one or more induced Hamiltonian subgraphs each of which contains at least k vertices for a given k , and if so, obtains such a partition. We then give an efficient parallelization of the algorithm. We also investigate the problem of partitioning arbitrary directed graphs into Hamiltonian ones.
21308144
EmoEars: An Emotion Recognition System for Mandarin Speech
In this paper, an emotion recognition system for mandarin speech is presented. Five basic human emotions including angry, fear, happy, neutral and sad are investigated. The recognizer is based on neural network with OCON and ACON architecture. Some novel feature selection methods are also added as optional tool to enhance the efficiency and classification accuracy. The system can train speaker dependent emotion speech model through online emotional utterance recording. Experiment results show that emotion can be recognized through neural network model, the best mean accuracy is 86.7%. In addition, the feature selection module is effective to reduce the compute load and increase the generalization ability of the recognizer.
11672412
Dealing with Node Mobility in Ad Hoc Wireless Network
A Mobile “Ad hoc” wireless NETwork (MANET) is a network established for a special, often extemporaneous service customized to applications. The ad hoc network is typically set up for a limited period of time, in an environment that may change from application to application. As a difference from the Internet where the TCP/IP protocol suite supports a vast range of applications, in the MANET the protocols are tuned to a specific customer and application (eg, send a video stream across the battlefield; find out if there is a fire in the forest; establish a videoconference among several teams engaged in a rescue effort, etc). The customers move and the environment may change dynamically and unpredictably. For the MANET to retain its efficiency, the ad hoc protocols at various layers may need to self-tune to adjust to environment, traffic and mission changes. From these properties emerges the vision of the MANET as an extremely flexible, malleable and yet robust and formidable network architecture. Indeed, an architecture that can be deployed to monitor the habits of birds in their natural habitat, and which, in other circumstances, can be organized to interconnect rescue crews after a Tsunami disaster, or yet can be structured to launch deadly attacks onto unsuspecting enemies.
7815226
Peak Load Forecasting Using the Self-organizing Map
This paper aims to study the short-term load forecasting of electricity by using an extended self-organizing map. We first adopt a traditional Kohonen self-organizing map (SOM) to learn time-series load data with weather information as parameters. Then, in order to improve the accuracy of the prediction, an extension of SOM algorithm based on error-correction learning rule is used, and the estimation of the peak load is achieved by averaging the output of all the neurons. Finally, as an implementation example, data of electricity demand from New York Independent System Operator (ISO) are used to verify the effectiveness of the learning and prediction for the proposed methods.