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| April 5
| April 5
| Jaishanker Pillai
| Jaishanker Pillai
|
| Sparsity Inspired Unconstrained Iris Recognition
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|-
| April 12
| April 12
Latent variables models have been widely applied in many problems in machine learning and related fields such as computer vision and information retrieval.However, the complexity of the latent space in such models is typically left as a free design choice. A larger latent space results in a more expressive model, but such models are prone to overfitting and are slower to perform inference with. The goal of this work is to regularize the complexity of the latent space and learn which hidden states are really relevant for the prediction problem.To this end, we propose regularization with a group norm such as L1-L2 to estimate parameters of a Latent Structural SVM. Our experiments on digit recognition show that our approach is indeed able to control the complexity of latent space, resulting in significantly faster inference at test-time without any loss in accuracy of the learnt model.
Latent variables models have been widely applied in many problems in machine learning and related fields such as computer vision and information retrieval.However, the complexity of the latent space in such models is typically left as a free design choice. A larger latent space results in a more expressive model, but such models are prone to overfitting and are slower to perform inference with. The goal of this work is to regularize the complexity of the latent space and learn which hidden states are really relevant for the prediction problem.To this end, we propose regularization with a group norm such as L1-L2 to estimate parameters of a Latent Structural SVM. Our experiments on digit recognition show that our approach is indeed able to control the complexity of latent space, resulting in significantly faster inference at test-time without any loss in accuracy of the learnt model.
===Sparsity Inspired Unconstrained Iris Recognition===
Speaker: [http://www.umiacs.umd.edu/~jsp/ Jaishanker Pillai] -- Date: April 5, 2012
Iris recognition is one of the most popular approaches for human authentication, since the iris patterns are unique for each person and remain stable for long periods of time. However, existing algorithms for iris recognition require clean iris images, which limit their utility in unconstrained environments like surveillance. In this work, we develop an unconstrained iris recognition algorithm by modeling the inherent structure in clean iris images using sparse representations. The proposed algorithm recognizes the test image and also predicts the quality of acquisition. We further extend the introduced algorithm by a quality based fusion framework, which combine the recognition results from multiple test images. Extensive evaluation on existing datasets clearly demonstrate the utility of the proposed algorithm for recognition and image quality estimation.
