# Contactless Fingerprint Recognition Using Deep Learning--A Systematic Review
## 6 Discussion  
This review is intended to provide a systematic survey of the deep-learning approaches employed for contactless fingerprint processing. The review surveyed eight full-text scientific research articles showing how deep learning can replace machine learning in contactless fingerprint contexts. This review was focused on three major research questions: the contactless finger photo capturing method, the classical approach of fingerprint recognition, and the use of deep learning. The first research question shows the direction of the different capturing methods and various camera sensors. This analysis might help researchers to understand divergent capturing methods and their limitations. Also, they might be motivated to employ smartphone-based finger photo capturing. In the second research question, we explained how classical machine-learning techniques have been introduced into contactless fingerprint recognition methods. This review covered feature extraction, image segmentation, and blur reduction; however, systems like data acquisition, data cleaning, data labeling, etc. were beyond the scope of this review. The main contribution of this paper is the review of the use of deep learning, specifically, its impact and usability in the field of contactless fingerprints. We have discussed how a contactless recognition system can benefit from using deep learning. In addition, we have also pointed out potential vulnerabilities in classical methods and shown the applicability of deep learning to real-world applications. Research Question 3 shows that the following factors can impact the research of contactless fingerprint recognition systems:  
Feature learning: Deep-learning methods have an advantage over previous state-of-the-art methods because they can learn features from data. Contactless fingerprint recognition systems require both local and global features [89] and are compatible with hierarchical and structural feature learning enabled by deep learning [90]. In addition to processing and labeling with the handcrafting data, some tools like labelme and image-label will be difficult in most cases. Therefore, deep learning can assist in preprocessing or extraction of the features of fingerprint images. The learned features can be generalized to previously unseen datasets and other related tasks (for example, features learned for contactless fingerprint recognition can also be used for fingerprint attribute estimation, e.g., ridge, minutiae pattern). In addition, pre-training improves feature-learning by large amounts of unlabeled data when using smaller training datasets. Concentration on Identification: Authentication and recognition have been the primary focus of deep-learning research in the contactless fingerprint context. Authentication is a comparably easy problem and estimates well for a large number of subjects. However, the more challenging part is the identification problem. The biometric system needs to distinguish between potentially millions of identities for large-scale identification. This system requires complex deep-learning architectures to capture definite interclass differences and handle large intra-class variability. Consequently, much training data would be required to capture these variations. Large-scale datasets: Though deep-learning approaches have already exceeded human performance on some in-the-wild, large-scale datasets, these datasets do not meet the requirements of real-world, high-security applications. In addition, there is a lack of large-scale datasets for contactless fingerprint modalities in biometrics to benefit from deep learning. Even if large datasets are available, each individual needs to have sufficient representative samples to consider for various influencing factors. Dataset quality: Existing fingerprint recognition datasets are mostly gathered from the public dataset. It is important to use large-scale datasets that capture real-world variations for biometrics to benefit from deep learning, especially in the contactless fingerprint area.