# Contactless Fingerprint Recognition Using Deep Learning--A Systematic Review
###### Abstract  
Contactless fingerprint identification systems have been introduced to address the deficiencies of contact-based fingerprint systems. A number of studies have been reported regarding contactless fingerprint processing, including classical image processing, the machine-learning pipeline, and a number of deep-learning-based algorithms. The deep-learning-based methods were reported to have higher accuracies than their counterparts. This study was thus motivated to present a systematic review of these successes and the reported limitations. Three methods were researched for this review: (i) the finger photo capture method and corresponding image sensors, (ii) the classical preprocessing method to prepare a finger image for a recognition task, and (iii) the deep-learning approach for contactless fingerprint recognition. Eight scientific articles were identified that matched all inclusion and exclusion criteria. Based on inferences from this review, we have discussed how deep learning methods could benefit the field of biometrics and the potential gaps that deep-learning approaches need to address for real-world biometric applications. biometrics; contactless fingerprint; deep learning; fingerprint analysis; fingerprint recognition +
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and inter-class variability in the captured fingerprint in terms of resolution, orientation, sensor noise, and skin conditions. A finger photo acquired by a contactless sensor does not suffer from deformation or latent, hidden fingerprints [7, 10]. However, new challenges are also present here. For example, captured images can be of poor quality, with different size, low resolution, background segmentation, or uncontrolled illumination, and face difficulty in extracting features like minutiae, finger enhancement, etc. [16]. According to the NIST, a standard fingerprint image, generally the frontal region of the finger, requires 500 dpi imaging sensors for a good-quality application [17]. These can be captured by smartphones or a handheld electronic device [7, 18, 19, 20]. The contactless finger image obtains ridge-valley contrast that is different from a print made from the contact of a finger on a live-scan capture device [24]. To address this, different technologies for acquiring finger photos, such as 2D and 3D fingerprints, have been introduced [13, 16, 25, 26]. Image processing can solve some issues, while the rest of them remain in contactless 2D and 3D fingerprint areas [27]. In recent years, deep-learning technology has demonstrated success in image recognition, classification, and feature representation [28, 29, 30, 31, 32, 33, 34, 35, 36, 37]. These deep-learning models have also been employed in contactless fingerprint-based biometric technologies [38]. It is necessary to conduct a comprehensive survey on the latest research findings on 2D and 3D contactless fingerprint recognition systems based on deep-learning technology to understand those models and point out the future development direction. It is useful to note that there is a particular system of capturing contactless finger photo images that might impact the performance of the deep-learning models. Also, it will be useful to know how the limitations of classical machine-learning open the door for deep learning in the contactless fingerprint area. This study explored deep neural network (DNN) methods for contactless fingerprint recognition. For this, we needed to analyze the machine learning (ML)-based algorithms to compare with DNN methods. Photo capture and image processing are the first steps for contactless fingerprint recognition. We have explored the steps of feature extraction and recognition based on ML and DNN methods. Various test outputs with their performance were analyzed to validate the feasibility of the suggested DNN methods. This paper has investigated fingerprint capturing methods, fingerprint preprocessing, and feature extraction in both classical image processing and machine learning, as well as replacement of classical methods by deep learning.