A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein Image

Abstract

As the second generation of biometric technology, finger vein recognition has become a research hotspot due to its advantages such as high security, and living body recognition. In recent years, the global pandemic has promoted the development of contactless identification. However, the unconstrained finger vein acquisition process will introduce more uneven illumination, finger image deformation, and some other factors that may affect the recognition, so it puts forward higher requirements for the acquisition speed, accuracy and other performance. Considering the universal, obvious, and stable characteristics of the original finger vein imaging, we proposed a new Region Of Interest (ROI) extraction method based on the characteristics of finger vein image, which contains three innovative elements: a horizontal Sobel operator with additional weights; an edge detection method based on finger contour imaging characteristics; a gradient detection operator based on large receptive field. The proposed methods were evaluated and compared with some representative methods by using four different public datasets of finger veins. The experimental results show that, compared with the existing representative methods, our proposed ROI extraction method is 1/10th of the processing time of the threshold-based methods, and it is similar to the time spent for coarse extraction in the mask-based methods. The ROI extraction results show that the proposed method has better robustness for different quality images. Moreover, the results of recognition matching experiments on different datasets indicate that our method achieves the best Equal Error Rate (EER) of 0.67% without the refinement of feature extraction parameters, and all the EERs are significantly lower than those of the representative methods.

Zero-FVeinNet: Optimizing Finger Vein Recognition with Shallow CNNs and Zero-Shuffle Attention for Low-Computational Devices

“…The ROI extraction stage is crucial in practical applications. The finger vein ROI extraction methodology can adopt the technique detailed in reference [46], which proposes a novel method for finger vein images. This method includes a horizontal Sobel operator, a contour-based edge detection method, and a gradient detection operator with a large receptive field.…”

Finger Vein Recognition Based on Multi-Features Fusion

“…The study performed on the MMCBNU_6000 dataset revealed that the highest classification accuracy using the CNN-RAW model is 91.72%. In the study performed by Lu et al [31] in 2021, a new region of interest extraction method was proposed depending on the characteristics of the FV image and compared with some representative methods. The equal error rates calculation of the proposed method on the MMCBNU_6000 dataset is 5.49% by using maximum curvature, and 3.33% by using repeated line tracking feature extraction method.…”

FV-EffResNet: an efficient lightweight convolutional neural network for finger vein recognition

“…Finger vein ROI extraction involves isolating and clipping out the finger region with the most abundant vein pattern and eliminating undesirable areas from the raw images. In this work, we utilized the method proposed by Lu et al (2021) , which is based on the characteristics of the original finger vein image. Their algorithm starts by searching for the finger edges based on the finger contour imaging characteristics.…”