Parallelizing Modules to Optimize and Enhance Fingerprint Recognition Systems

Saraansh Tandon


Today human identification is a practice followed quite frequently in contexts ranging from forensic labs to high security organisations to almost every domestic household. One of the oldest and most accurate ways to establish the identity of an individual is through fingerprints. It is so because, biologically no two people (not even identical twins) have the same fingerprints. Hence it comes as no surprise that fingerprint biometrics has been a very active domain of research for a long time. Over the years, especially with the emergence of the deep learning era, the computational modules of a fingerprint recognition system have become bigger and bigger. Simultaneously, the actual hardware deploying fingerprint recognition systems has become more and more user friendly. These developments provide a contrast as one tries to incorporate the highly accurate but also extremely heavy modules of a fingerprint recognition system in the palm of our hands. In this thesis, we will perform a couple of studies which explore ways to optimize fingerprint recognition systems. This is done by trying to parallelize modules and eliminate semantically redundant computations. Along the way we will also obtain better performances as compared to the academic state of the art. Typical fingerprint recognition systems are comprised of a spoof detection module and a subsequent matching module, running one after the other. In the first study, we posit that both spoof detection and fingerprint matching are correlated tasks. Therefore, rather than performing the two tasks separately, we propose a joint model for spoof detection and matching to simultaneously perform both tasks without compromising the accuracy of either task. In practice, this reduces the time and memory requirements of the fingerprint recognition system by 50% and 40%, respectively. On the other hand, fingerprint matching task itself can be solved in multiple ways. Fingerprint feature extraction is a core step in the fingerprint matching task and it can be solved by using a global as well as a local approach. State of the art global approaches use huge deep learning models to process the full fingerprint image at once which makes the corresponding approach memory intensive. Whereas local approaches involve minutiae based patch extraction and multiple feature extraction steps which make the corresponding approach time-intensive. But both these approaches provide useful and sometimes exclusive insights for solving the problem. Hence using both kinds of approaches together for extracting fingerprint representations is semantically useful but quite inefficient. Hence, in the second study, we use a convolutional transformer based approach with an in-built minutiae extractor. This study provides an efficient solution for feature extraction that parallely provides a global as well as a local represen tation of a fingerprint. This not only provides a replacement for using a heavy combination of two independent local and global approaches but also provides a further 54.41% speedup over the local approach and a 57.93% conservation in memory as compared to the global approach. Both of these studies work towards the common goal of optimizing fingerprint recognition systems and hence provide a significant advantage in the context of such systems deployed on resource constrained devices like smartphones.

Year of completion:  June 2022
 Advisor : Anoop M Namboodiri

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