# FVR-Net: Finger Vein Recognition with Convolutional Neural Network Using Hybrid Pooling
## 3 The Proposed Model  
### Data Preprocessing  
Good performance from finger vein recognition systems is usually attributed to the quality of the vein images [25]. Therefore, raw vein images acquired from infrared image sensors should be subjected to preprocessing prior to feature extraction. The preprocessing stage is intended to discard any nonideal entities appearing in the vein images, such as noise, shadows, and low contrast due to a rotational or translational variant property of the finger or a faulty acquisition device.  
Figure 1a,b, respectively, depict the architectural pipeline of the data preprocessing strategy in our method and the preprocessed images obtained at each corresponding step. Because the input images from the publicly available datasets are presented at various sizes and orientations, the preliminary step is to accomplish ROI selection of the intended part of the raw image. This guarantees dimensional uniformity in all images entering the CNN for feature extraction. At this particular stage, all images are subjected to normalization, where they are subsampled to the desired dimension by cropping them to 100 \(\times\) 300 pixels. Next, the cropped images undergo image enhancement with contrast limited adaptive histogram equalization (CLAHE) [38], where the intensities of the images are adjusted in such a way that they are better distributed on the histogram. By doing so, the global contrast of the image is improved as the most frequent intensity values are effectively spread, while the lower contrast areas gain higher values.  
After that, a Gabor filtering technique [5] is implemented to enhance the concrete vein features. A Gabor filter is a special form of bandpass filter suited for segmentation of texture that possesses optimal localization properties in both the spatial and frequency domain of an image. The filter reduces the impact of noise on the images to enable good feature extraction and ultimately enhances recognition accuracy. Furthermore, to enhance the clarity of patterns, vein images are subjected to morphological operations. These operations tend to discard the irrelevancies while preserving the vital shape characteristics of the image [39]. Within the morphological operations, a grayscale erosion technique is employed, followed by grayscale dilation. The erosion and dilation transformation bear a marked similarity when acquiring perspective imagery of the vein image. When applied iteratively as a pair, they enable smoothing of contour, and they eliminate small gaps in the vein patterns without any geometric distortion of the global features. In the last stage of data preprocessing, to coarsely localize the vein patterns in the images, a  
Figure 1: Data preprocessing steps. (**a**) Preprocessing block diagram, and (**b**) preprocessed images at each step in the order of preprocessing steps.  
binarization technique with a threshold of 110 is applied. The pixel values greater than 110 represent the vein pattern in white, while the rest represent background pixels in black. After completion of the preprocessing stage, images are immediately passed to the feature extraction procedure, followed by classification of the extracted features.