Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multichannel Approach to Fingerprint Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Fingerprint Classification by Combination of Flat and Structural Approaches
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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We present new fingerprint classification algorithms based on two machine learning approaches: support vector machines (SVMs), and recursive neural networks (RNNs). RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features which can be integrated in the SVMs. SVMs are combined with a new error correcting code scheme which, unlike previous systems, can also exploit information contained in ambiguous fingerprint images. Experimental results indicate the benefit of integrating global and structured representations and suggest that SVMs are a promising approach for fingerprint classification.