Abstract
Conventional fingerprint verification systems use only static information. In this paper, fingerprint videos, which contain dynamic information, are utilized for verification. Fingerprint videos are acquired by the same capture device that acquires conventional fingerprint images, and the user experience of providing a fingerprint video is the same as that of providing a single impression. After preprocessing and aligning processes, “inside similarity” and “outside similarity” are defined and calculated to take advantage of both dynamic and static information contained in fingerprint videos. Match scores between two matching fingerprint videos are then calculated by combining the two kinds of similarity. Experimental results show that the proposed video-based method leads to a relative reduction of 60 percent in the equal error rate (EER) in comparison to the conventional single impression-based method. We also analyze the time complexity of our method when different combinations of strategies are used. Our method still outperforms the conventional method, even if both methods have the same time complexity. Finally, experimental results demonstrate that the proposed video-based method can lead to better accuracy than the multiple impressions fusion method, and the proposed method has a much lower false acceptance rate (FAR) when the false rejection rate (FRR) is quite low.
Highlights
In ancient China and many other countries and districts, people had been aware that a fingerprint can be used for identity authentication [1]
With the rapid expansion of fingerprint recognition in forensics, operational fingerprint databases became so huge that manual fingerprint identification became infeasible, which led to the development of Automatic Fingerprint Identification Systems (AFIS) using a computer for fingerprint verification [2]
The performance of a fingerprint verification system is mainly described by two values, i.e., false acceptance rate (FAR) and false rejection rate (FRR)
Summary
In ancient China and many other countries and districts, people had been aware that a fingerprint can be used for identity authentication [1]. 3 features, such as pores and ridge contours extracted from high resolution fingerprint images, are employed for fingerprint recognition, and the performance gain by introducing level 3 features is studied [3,27,28,29,30] All of these methods use static information (information from one static impression or from several temporal-independent static impressions), and no dynamic information (information from a video) is introduced. They detected the distortion of fingerprint impressions due to excessive force and the positioning of fingers during image capture They investigate two aspects of dynamic behaviors from video and propose a new type of biometrics, named “resultant biometrics”.
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