Abstract
Most of the existing vein recognition algorithms are only effective for specified datasets, and once replacing the vein image acquisition device, i.e., the properties of the collected vein images are changed, the performance of the algorithm will be degraded greatly. Therefore, a transfer Nonnegative Matrix Factorization (NMF) based vein recognition algorithm is proposed, which makes vein features more universal. Its contributions are mainly reflected in the following two aspects: 1) The orthogonal constraint is imposed on the model to reduce the redundancy between feature bases and increase the difference between the features of different veins; 2) The differences between the vein features in different datasets are reduced based on Maximum Mean Difference (MMD) constraint, i.e., the knowledge of the source dataset is transferred to the target dataset well, and the universality of vein features can be improved. Experimental results show that the proposed algorithm outperforms state of the art methods on two dorsal hand vein datasets and two finger vein datasets.
Highlights
Since vein recognition has the advantages such as living recognition, internal feature, non-contact acquisition and special light source at the same time, it has become an important biometric recognition technology [1]
To make a vein recognition system work well, the common method is that a vein image acquisition device needs to be selected firstly which can be represented by Device A; a large number of vein images are collected using the device and annotated manually; in final, the proper feature extractor and classifier can be acquired through training
To solve the above problem, we proposed a novel vein recognition algorithm with feature transferability, which can transfer the knowledge from the source recognition system to the target recognition system by using a small number of samples collected by the target system
Summary
Since vein recognition has the advantages such as living recognition, internal feature, non-contact acquisition and special light source at the same time, it has become an important biometric recognition technology [1]. To make a vein recognition system work well, the common method is that a vein image acquisition device needs to be selected firstly which can be represented by Device A; a large number of vein images are collected using the device and annotated manually; in final, the proper feature extractor and classifier can be acquired through training. There is no unified image acquisition device for vein recognition currently, and the properties of the collected images by different devices may be different from each other, we think that the feature extractor and classifier based on Device A are difficult to be suitable for the system using Device B. To solve the above problem, we proposed a novel vein recognition algorithm with feature transferability, which can transfer the knowledge from the source recognition system to the target recognition system by using a small number of samples collected by the target system.
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