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| November 21
| November 21
| Arunkumar Mohananchettiar
| Jonghyun Choi
| TBA
| Renaissance of Convolutional Neural Network - what, why and so?
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| November 28
| November 28
In this talk, I will present the work on feature-level fusion method for multimodal biometric recognition. Traditional methods for combining outputs from different modalities are based on score-level or decision-level fusion. Feature-level fusion can be more discriminative, but has hardly been explored due to challenges of different feature outputs and high feature dimensions. Here, I will present a framework using joint sparsity to combine information, and show its application to multimodal biometric recognition, face recognition and vidoe-based recognition.
In this talk, I will present the work on feature-level fusion method for multimodal biometric recognition. Traditional methods for combining outputs from different modalities are based on score-level or decision-level fusion. Feature-level fusion can be more discriminative, but has hardly been explored due to challenges of different feature outputs and high feature dimensions. Here, I will present a framework using joint sparsity to combine information, and show its application to multimodal biometric recognition, face recognition and vidoe-based recognition.
===Renaissance of Convolutional Neural Network - what, why and so?===
Speaker: [http://www.umiacs.umd.edu/~jhchoi/ Jonghyun Choi] -- Date: November 21, 2013
The convolutional neural network based deep networks recently improve image classification accuracy significantly over the state-of-the-art vision approaches. I will go through what the successful deep convolutional neural net looks like, why it is again popular now and on-going deep net research in other research groups. I will mostly go through the successful instance of deep convolutional neural net tuned by Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton, published in NIPS 2012.
