Faculty Publications
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Item Recognition of emotions from video using acoustic and facial features(Springer-Verlag London Ltd, 2015) Sreenivasa Rao, K.S.; Koolagudi, S.In this paper, acoustic and facial features extracted from video are explored for recognizing emotions. The temporal variation of gray values of the pixels within eye and mouth regions is used as a feature to capture the emotion-specific knowledge from the facial expressions. Acoustic features representing spectral and prosodic information are explored for recognizing emotions from the speech signal. Autoassociative neural network models are used to capture the emotion-specific information from acoustic and facial features. The basic objective of this work is to examine the capability of the proposed acoustic and facial features in view of capturing the emotion-specific information. Further, the correlations among the feature sets are analyzed by combining the evidences at different levels. The performance of the emotion recognition system developed using acoustic and facial features is observed to be 85.71 and 88.14 %, respectively. It has been observed that combining the evidences of models developed using acoustic and facial features improved the recognition performance to 93.62 %. The performance of the emotion recognition systems developed using neural network models is compared with hidden Markov models, Gaussian mixture models and support vector machine models. The proposed features and models are evaluated on real-life emotional database, Interactive Emotional Dyadic Motion Capture database, which was recently collected at University of Southern California. © 2013, Springer-Verlag London.Item Scalable and fair forwarding of elephant and mice traffic in software defined networks(Elsevier, 2015) Hegde, S.; Koolagudi, S.; Bhattacharya, S.A software defined network decouples the control and data planes of the networking devices and places the control plane of all the switches in a central server. These flow based networks do not scale well because of the increased number of switch to controller communications, limited size of flow tables and increased size of flow table entries in the switches. In our work we use labels to convey control information of path and policy in the packet. This makes the core of the network simple and all routing and policy decisions are taken at the edge. The routing algorithm splits the elephant traffic into mice and distributes them across multiple paths, thus ensuring latency sensitive mice traffic is not adversely affected by elephant traffic. We observed that label based forwarding and traffic splitting work well together to enable scalable and fair forwarding. Our approach is topology independent. We present here a few preliminary simulation results obtained by running our routing algorithm on random network topologies. © 2015 Elsevier B.V.