# Contactless Fingerprint Recognition Using Deep Learning--A Systematic Review
## 4 Classical Method to Extract Features from Contactless Fingerprints
This effect works best with images obtained with a very small finger-to-sensor distance and a wide-open aperture. One experiment showed that the variance-modified Laplacian of Gaussian (VMLOG) algorithm is best suited for contactless 2D fingerprint-capturing devices [10, 66, 67]. All finger shapes, from thumb to little finger, are highly similar for all finger position codes. A machine-learning-based algorithm has been applied to a binarized image in the LUV color model [10]. To make the skin color contrast and segment the skin and background color, the analysis of the YCbCr color space represents a very vital approach [64, 68]. A different method of image segmentation and image depth information approach combined an RGB image via a smartphone [69]. These were able to extract the finger slap (the four fingers except the thumb) from busy backgrounds for further processing. Minutiae-Based Feature Extraction: One of the main conditions for pre-processed contactless fingerprints is that images must be converted from RGB to greyscale [70]. Thus, ROI such as minutiae, ridge valley extraction, and finger orientation estimation must also be handled with a machine-learning approach. After detecting the finger, the ROI must be extracted, which involves the normalization of width, height, and resolution. This 3D contactless fingerprint preprocessing stage implies an extracted finger image as input. It should be noted that finger detection and ROI extraction are done in output. The color-based segmentation of ROI extraction constrained setups depends on contactless 3D finger geometry [10]. Several operations used the ridge-line orientation and shape to detect the core point [71]. Using a support vector machine (SVM) [72], it is easy to classify minutiae-based fingerprints and to detect the minutiae points as the detection points to refer to as a category [73]. SVM can determine the image quality with five feature vector lengths such as gray mean, gray variance, contrast, coherence, and the main energy ratio. These features take much training time to implement. Fingerprint Image Enhancement: To improve image contrast and sharpness, image enhancement techniques such as spatial domain techniques and frequency domain techniques can be used to improve the quality [74, 75]. Finger image enhancement should result in a fingerprint image with uniform illumination. Three different methods to achieve this appeared in the literature: a normalization using mean and variance filters [30], histogram enhancements like contrast-limited adaptive histogram equalization (CLAHE) [76], and local binary patterns (LBP) for enhancing the ridge-valley contrast of the 2D contactless fingerprint system [77]. Reducing the blurred image from the original image is another challenge in contactless 3D fingerprint enhancement [14]. A combination of image-processing algorithms and machine learning for extracting sweat pores of fingerprint patterns level-3 has been proposed by [PERSON] et al. [78]. Analyzing the Deep Neural Networks Methods Proposed for the Contactless Fingerprint Recognition Systems  
Simple convolutional and pooling layers were utilized to create deep-learning models in many articles, but a multi-task fully convolutional network was used in three of them. The architecture for the multi-task deep convolutional network is shown in Figure 8.  
Matching the contactless fingerprint with a traditional contact-based fingerprint using deep learning is a new domain in biometrics research. In order to recognize contactless fingerprints, this paper [67] described a convolutional neural network (CNN) framework. The convolutional and pooling layers are the two main layers of the algorithm. The convolutional layers were used to execute low-level features such as edges, corners, etc. Pooling layers enabled correct operations such as reducing the dimension of feature maps. Ten images were provided to the CNN model as an input batch for training. A training accuracy of 100 percent was attained after four iterations.