Abstract
Recent developments enable biometric recognition systems to be available as mobile solutions or to be even integrated into modern smartphone devices. Thus, smartphone devices can be used as mobile fingerprint image acquisition devices, and it has become feasible to process fingerprints on these devices, which helps police authorities carry out identity verification. In this paper, we provide a comprehensive and in-depth engineering study on the different stages of the fingerprint recognition toolchain. The insights gained throughout this study serve as guidance for future work towards developing a contactless mobile fingerprint solution based on the iPhone 11, working without any additional hardware. The targeted solution will be capable of acquiring 4 fingers at once (except the thumb) in a contactless manner, automatically segmenting the fingertips, pre-processing them (including a specific enhancement), and thus enabling fingerprint comparison against contact-based datasets. For fingertip detection and segmentation, various traditional handcrafted feature-based approaches as well as deep-learning-based ones are investigated. Furthermore, a run-time analysis and first results on the biometric recognition performance are included.
Highlights
Authorities responsible for the maintenance of public order, law enforcement, and civil security in the street sometimes have to determine people’s identities
A typical biometric recognition system consists of several modules, as shown in Figure 1, where the first and most crucial step is the capturing of biometric sample data
Looking at the fingertip detection task, we can see that the best-performing approaches are Single Shot Multibox Detector (SSD) MobileNet Quantized Greyscale (0.915) and Mask R-convolutional neural network (CNN) RGB (0.949)
Summary
Authorities responsible for the maintenance of public order, law enforcement, and civil security in the street (mainly police authorities) sometimes have to determine people’s identities. To establish the true identity of a person, technological advances offer authorities a wide range of possibilities. Most current techniques are based on biometric recognition technology. Afterwards, the captured data are processed and transmitted to the comparison system and a response will be sent back, and the whole process is expected to run in a reasonable time. There are only very few dedicated mobile fingerprint (FP)-capturing systems available to Austrian police officers. In order to establish the identity of a person, the police officer must transport the person to the nearest police station, where the FPs can be taken either using a contact-based optical scanner or using the classical ink-based technique. Establishing the identity of a person is a time-consuming task, and obviously, police forces and authorities wish to improve (speed up) this process
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