Objective

2

Finger vein recognition

an emerging biometric identification technology

has attracted the attention of numerous researchers. However

the quality of several collected finger vein images is not ideal due to individual differences

changes in the collection environment

and differences in the performance of acquisition equipment. In a finger vein recognition system

low-quality images seriously affect feature extraction and matching

resulting in poor identification performance of the system. In an application scene that requires the establishment of a standard template library of personal finger vein information in real life

registered low-quality images seriously influence the use of the finger vein standard template library. Therefore

correct quality assessment after collecting finger vein images is necessary to filter low-quality images and select high-quality ones to be inputted to a finger vein recognition system or to register a finger vein standard template library. To address the problems of considerable computation complexity

weak robustness

and unsatisfactory expression and the issue that the hand-crafted finger vein quality characteristic is sensitive to various factors

we develop a finger vein quality assessment method. These problems are addressed via multi-feature fusion

which is primarily based on the cascaded fine-tuning convolutional neural network (CNN).

Method

2

Finger vein image quality assessment methods based on deep learning require many labeled finger vein images. However

existing finger vein image public databases only provide finger vein images and do not mark them for quality. Thus

the first step should be labeling. In this study

the public finger vein database MMCBNU_6000 is labeled for quality representation in a semi-automated manner. This manner is based on the calculation of the number of veins in a finger vein image

followed by manual correction. Such an annotation method is more accurate

time saving

and cost effective than a pure manual annotation method. However

the collected low-quality finger vein images are fewer than the high-quality finger vein images in the actual scene; hence

the R-SMOTE algorithm is employed to balance all categories. The excellent capabilities of deep neural networks have been proven in the fields of image and speech. However

with regard to finger vein quality assessment

most existing methods are based on hand-crafted features

and only a few methods gain quality features via self-learning. In this study

the CNN structure in deep learning is applied to finger vein quality assessment

and the depth of the CNN framework is investigated for its contribution to quality representation. Deeper networks may not be good at representing the quality characteristics of finger vein images. The best network depth is confirmed after an experiment and used as the basis for subsequent research. Meanwhile

inspired by the combination of binary and grayscale images in traditional quality evaluation

two models

namely

multi-column CNN (MC-CNN) and cascaded fine-tuning CNN (CF-CNN)

are designed to merge the quality features of grayscale and binary images. When MC-CNN is trained and tested

binary and grayscale images must be inputted together to the model. As for CF-CNN

binary and grayscale images are inputted to the model in stages during training

and only the grayscale image is inputted during testing. Notably

we input the binary finger vein image to the network and verify that the quality characteristics of the binary finger vein help distinguish high-and low-quality finger vein images. After verification

we obtain a basis to believe that the combination of binary and grayscale images through CNN produces remarkable results.

Result

2

Several experimental results for testing are set on the MMCBNU_6000 database. The classification accuracy rates of the CNN-

K

(

K

=3

4

5) designed in this study are 93.31%

93.94%

and 85.63%

respectively; the classification accuracy rates of CNN-4 with grayscale and binary images as the input are 93.94% and 91.92%

and the classification accuracy rates of MC-CNN and CF-CNN are 91.44% and 94.62%

respectively. The experimental results of the simple CNN structure show that CNN-3 has the highest classification accuracy rate for high-quality images

CNN-5 has the highest classification accuracy rate for low-quality images

and CNN-4 has the highest classification accuracy rate for the entire test set. The experimental results of CNN-4 show that the grayscale vein form performs better than the binary vein form. Meanwhile

the experimental results of the complex CNN structure show that CF-CNN performs better than MC-CNN. Compared with other existing algorithms

CF-CNN has the highest classification accuracy rate for high-quality

low-quality

and overall test images on the MMCBNU_6000 database.

Conclusion

2

First

three simple CNN structures are designed and used for finger vein quality assessment. The comprehensive performance of CNN-4 is found to be better than that of CNN-3 and CNN-5

indicating that the network is not as deep as possible

and the structure of the network should be adjusted to suit the research questions. Second

the performance difference when gray and binary images are used for the same network is compared. Results show that both images characterize the vein quality to varying degrees. Finally

to fuse the quality features of grayscale and binary images

two fusion models (MC-CNN and CF-CNN) are proposed. CF-CNN

an end-to-end quality evaluation model of finger veins

is better than MC-CNN and has a simpler structure. In summary

our method demonstrates state-of-the-art performance and obtains better features than those from existing manual and single vein forms.