Training Invariant Support Vector Machines
Machine Learning
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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The Journal of Machine Learning Research
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UIC '09 Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing
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Iris recognition is a prosperous biometric method, but some technical difficulties still exist especially when applied in embedded systems. Support Vector Machine (SVM) has drawn great interests recently as one of the best classifiers in machine learning. In this paper, we develop an iris recognition system using SVM to classify the acquired features series. Even though the SVM outperforms most of other classifiers, it works slowly, which may hinder its application in embedded systems, where resources are usually limited. To make the SVM more applicable in embedded systems, we make several optimizations, including Active Learning, Kernel Selection and Negative Samples Reuse Strategy. Experimental data show that the method presented is amenable: the speed is 5 times faster and the correct recognition rate is almost the same as the basic SVM. This work makes iris recognition more feasible in embedded systems. Also, the optimized SVM can be widely applied in other similar fields.