Finger-Vein Quality Assessment by Representation Learning from Binary Images
Finger-vein quality assessment is an important issue in finger-vein verification systems as spurious and missing features in poor quality images may increase the verification error. Despite recent advances, current solutions depend on domain knowledge and are typically driven by visual inspection. In this work, we propose a deep Neural Network (DNN) for representation learning from binary images to predict vein quality. First, driven by the primary target of biometric quality assessment, i.e. verification error minimization, we assume that low quality images are false rejected finger-vein images in a verification system. Based on this assumption, the low and high quality images are labeled automatically. Second, as image processing approaches such as enhancement and segmentation may produce false features and ignore actual ones thus degrading verification accuracy, we train a DNN on binary images and derive deep features from its last hidden layer for quality assessment. Our experiments on two large public finger-vein databases show that the proposed scheme accurately identifies high and low quality images and significantly outperform existing approaches in terms of the impact on equal error rate (EER) improvement.
KeywordsBiometrics Finger-vein quality assessment Deep learning Deep neural network Representation learning Feature representation
This work is supported by the Direction générale des Entreprises (DGE) of Ministère de l’économie, de l’industrie et du numérique(Project IDEA4SWIFT 12028), the National Natural Science Foundation of China(Grant No. 61402063), the Natural Science Foundation Project of Chongqing (Grant No. cstc2013kjrc-qnrc40013), and the Scientific Research Foundation of Chongqing Technology and Business University(Grant No. 1352019; Grant No. 2013-56-04).
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