# Contactless Fingerprint Recognition Using Deep Learning--A Systematic Review
## 4 Classical Method to Extract Features from Contactless Fingerprints
At 95 percent of testing accuracy, 140 out of 275 images were used for testing purpose. A fully convolutional network was applied for minutiae detection and extraction in [79]. The minute point and its corresponding direction were processed and analyzed using contactless grayscale fingerprint images from two different public datasets [12, 80]. Images were assessed online after being trained offline. A full-sized contactless fingerprint from two different datasets (9000, 6000) was applied as an input and its corresponding minute ground truth was indicated as an output in the offline portion. In conjunction with a novel loss function, this method concurrently learns the minutiae detection and orientation. One of the main claims of this study is that a multi-task technique outperforms any single minutiae detection task. An hourglass-shaped encoder-decoder network [81] structure was applied for a multi-task deep neural network called ContactlessMinuNet architecture [79]. To process the input fingerprint images, a shared encoder subnetwork was used. For up-sampling, the subnetwork was decoded to expand the image. Lastly, the network split into two branches for minutiae detection and direction computation. Minutiae point detection branch: In this network [79], the input feature represents the detection of minutiae points, and the output represents the probability of each pixel's minutiae points. The network is very simple, with a 1 \(\times\) 1 convolutional layer, a batch normalization layer that standardizes the input layer, and a sigmoid layer. A non-linear activation function sigmoid layer is used to generate minutiae location. Minutiae direction regression branch: This network [79] is designed to predict the minutiae direction. Pixel-by-pixel images were extracted with a phase angle \(\omega\)\(\xi\) (0,2\(\pi\)). The subnetwork works as input features, and the output layer predicts minutiae direction. A convolutional layer (1 \(\times\) 1), batch normalization, and non-linear activation function _tanh_ layer were used to predict the minutiae path. Using 3 \(\times\) 3 CNN layers, the final convolution has been used with (stride = 1) and padding to keep the height and width constant. For testing, the proposed method was compared with the benchmark dataset of the PolyU dataset [12]. The accuracy of minutiae detection and its location increased to 94.10% compared to 89.61%. The proposed method of this study is shown in Figure 9.  
Figure 8: An architecture of multi-task deep convolutional networks [79]. A study reported in [82] suggested how to extract a minutiae point from an input image without preprocessing. To train the model and obtain the output without any preprocessing, a number of deep neural networks have been deployed. Initially, JudgeNet was trained to locate the minutiae regions and picked a general overview of detecting minutiae points. The original image resolution was 640 \(\times\) 640 and 500 ppi with 200 labeled images. A max pooling was used to reduce the image dimensions, and it showed image dimensions of 45 \(\times\) 65. Using multiscale input and four CNN layers, the network performed very well to get the accurate output. Later, another deep CNN layer named LocalNet specifically indicated the directions of minutiae with a more concise image dimension of 45 \(\times\) 45 and decided the specific location. Lastly, a comprehensive estimation and decision were made to add or eliminate the minutiae location. The overview of the network architecture is shown in Figure 10.  
A method was proposed [83] to get the proper position of the contactless fingerprint of multi-view 3D fingerprint features using CNN. A fully convolutional network (FCN) was applied with this model for automatic fingerprint segmentation and three Siamese networks for fingerprint multi-view. Various convolutional neural network models such as VGG net [31], AlexNet [84], and GoogleNet [85] are introduced in this work. These architectures are very well-trained pixel-to-pixel deep networks.