Robust fingerprint classification with Bayesian convolutional networks

Abstract

Fingerprint classification is vital for reducing the search time and computational complexity of the fingerprint identification system. The robustness of classifier relies on the strength of extracted features and the ability to deal with low-quality fingerprints. The proficiency to learn accurate features from raw fingerprint images rather than explicit feature extraction makes deep convolutional neural networks (DCNNs) attractive for fingerprint classification. The DCNNs use softmax for quantifying model confidence of a class for an input fingerprint image to make a prediction. However, the softmax probabilities are not a true representation of model confidence and often misleading in feature space that may not be represented with the available training examples. The primary goal of this study is to improve the efficacy of the fingerprint classification by dealing with false positives by employing Bayesian model uncertainty. The efficacy of the proposed method is shown through experimentations on NIST special database 4 (NIST-4) and fingerprint verification competition 2002 database 1-A (FVC DB1-A) 2002 and 2004 datasets. Results show that 0.8-1.0% of accuracy is improved with model uncertainty over the conventional DCNN.