# Contactless Fingerprint Recognition Using Deep Learning--A Systematic Review
## 4 Classical Method to Extract Features from Contactless Fingerprints  
The basic steps for contactless fingerprint recognition pipeline are shown in Figure 7 as a flowchart:  
Most of the contact-based fingerprint images captured from the devices are grayscale and ready for feature extraction [62]. In contrast, most contactless finger-imaging solutions provide color RGB images that require preprocessing before feature extraction [63]. The primary challenges of preprocessing contactless finger images and recommended methods to overcome these challenges are shown in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Capturing Device** & **Authors** & **Cost** & **Light Environment** & **Finger Type** \\ \hline
**Mobile Phone (2D)** & [PERSON] et al. [19] & Low cost & No extra illumination & Single Finger \\ \hline
**Digital Camera (2D)** & [PERSON] et al. [59] & Low cost & Table lamp illumination & Single Finger \\ \hline
**Digital Camera (2D)** & [PERSON] et al. [60] & Medium cost & Green Light illumination & Finger slap \\ \hline
**Webcam (2D)** & [PERSON] et al. [61] & Low cost &
\begin{tabular}{c} Different illumination (white light, \\ no light) \\ \end{tabular} & Single Finger \\ \hline
**Webcam** & [PERSON] and [PERSON] [9] & Low cost & No illumination & Finger slap \\ \hline
**Smartphone (2D)** & [PERSON] et al. [18] & Low cost & No illumination & Finger slap \\ \hline
**Smartphone (2D)** & [PERSON] et al. [49] & Low cost &
\begin{tabular}{c} Screen guidance. If flash required \\ (Y/N) \\ \end{tabular} & Finger slap \\ \hline
**Smartphone (2D)** & [PERSON] et al. [61] & Medium cost &
\begin{tabular}{c} 3 smartphones in different \\ illumination \\ \end{tabular} & Thumb and index finger \\ \hline
**Smartphone (3D)** & [PERSON] et al. [51] & Medium cost & 2 cameras with depth information & Finger slap \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of contactless 2D and 3D capturing devices and their properties. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Challenge** & **Authors** & **Year** & **Approach** \\ \hline Finger Segmentation & [PERSON] et al. [62] & 2017 & Hand color estimation in YCbCr \\ \hline Rotated pitched principal orientation & [PERSON] et al. [9] & 2015 & Artificial neural network \\ \hline Low contrast & [PERSON] et al. [62] & 2016 & CLAHE and extensions \\ \hline Distance to the sensor, ridge line frequency & [PERSON] et al [9] & 2017 & \begin{tabular}{c} Frequency map, sensor-finger distance \\ approximation \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Core/principal singular point detection \\ \end{tabular} & [PERSON] et al. [64] & 2010 &
\begin{tabular}{c} Poincare-based ridge orientation analysis \\ \end{tabular} \\ \hline Deformation correction & [PERSON] et al. [11] & 2018 & Robust thin-plate splines, deformation correction \\ model \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of challenges during the preprocessing of contactless finger images and proposed approaches. Figure 7: The fundamental steps for contactless fingerprint recognition from input to (**a**) preprocess the images; (**b**) Feature extraction (**c**) Dimension reduction; and (**d**) classified the person. When processed with the classical methods, both contactless 2D and 3D images have issues with low focus of ridge/valley and blurred ROI (background) [57]. Misplaced or rotated fingers and the lack of skin deformation also cause processing issues [65]. An image processing pipeline must be developed based on the selected equipment and the environmental conditions needed during the image acquisition process. Image processing begins with the following common preprocessing steps:  
Finger Segmentation and Detection: The initial step is to detect the finger based on color and shape. Sharpness, shape, color contrast, and image depth information are four different categories for improving contactless 2D and 3D finger detection and image segmentation [13]. Sharpness-based strategies utilize the difference between the focused, blurred background and the sharp finger area.