Improving the Efficiency of Fingerprint Recognition Systems

Additya Popli

Abstract

Humans have used different characteristics to identify each-other since early times. This practice of identification based on person-specific features called biometric traits has developed over time to use more sophisticated techniques and characteristics like fingerprints, irises and gait in order to improve the identification performance. Fingerprints due to their distinctiveness, persistence over time and ease of capture, have become of the most widely use biometric traits for identification. However, with this ever increasing dependence on fingerprint biometrics, it is very important to ensure the safety of these recognition system against potential attackers. One of the most common and successful ways to circumvent these systems is through the use of fake or artificial fingers synthesized using commonly available materials like silicon and clay to match the real fingerprint of any particular person. Most fingerprint recognition systems employ a spoof detection module to filter out these fake fingerprints. While they seem to work well in general, it is a well-established fact that spoof detectors are not able to identify spoof fingerprints synthesized using ”unseen” or ”novel” spoof materials, i.e, the materials which were not available during the training phase of the detector. While it is possible to synthesize a few fingers using the various available materials, present-day spoof detectors require a large amount of samples for their training, which is practically not feasible due to the high cost and high complexity of fabrication of spoof fingers. In this thesis, we propose a method for creating artificial fingerprint images using only a very limited number of artificial fingers created from a specific material. We train a style-transfer network using available spoof fingerprint images which learns to extract material properties from the image, and then for each material, uses the limited set of spoof fingerprint images to generate a huge dataset of artificial fingerprint images without actually fabricating spoof fingers. These artificial fingerprint images can then be utilised by the spoof detector for training. Through our experiments, we show that the use of these artificially generated spoof images for training can improve the performance of existing spoof detectors over unseen spoof materials. Another major limitation of present-day recognition systems is their high resource requirements. Most fingerprint recognition systems use a spoof detector as a separate system either in series or in parallel with a fingerprint matcher leading to very high memory and time requirements during inference. To overcome this limitation, we explore the relationship between these two tasks in order to develop a common module capable of performing both spoof detection and matching. Our experiments show a high level of correlation between the features extracted for spoof detection and matching. We propose a new joint model which achieves similar fingerprint spoof detection and matching performance on various datasets as current state-of-the-art methods while using 50% less time and 40% less memory, thus providing a significant advantage for recognition systems deployed on resource-constrained devices like mobile phones.

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