| Author: | Tan, Hanzhuo |
| Title: | Contactless fingerprint identification and interoperability with contact-based fingerprints |
| Advisors: | Kumar, Ajay (COMP) |
| Degree: | M.Phil. |
| Year: | 2021 |
| Subject: | Fingerprints -- Identification -- Data processing Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Computing |
| Pages: | xi, 80 pages : color illustrations |
| Language: | English |
| Abstract: | Contactless fingerprint identification offers significantly higher user convenience, hygiene and has attracted increasing attention for the deployments. However, the presentation of fingers towards the contactless fingerprint sensors is hard to control and often results in unwanted pose changes that significantly degrade the contactless fingerprint matching accuracy. In addition, interoperability between contactless and conventional contact-based fingerprint recognition systems is critical for the success of emerging contactless fingerprint technologies. Nevertheless, image formation differences and acquisition distortions between these two modalities pose significant challenges for such interoperability. In order to address such problems and improve the contactless fingerprint matching accuracy, this thesis first proposes a more precise minutiae extraction and pose-compensation approach. As compared with the conventional minutiae extraction approaches, our deep neural network-based approach does not require any image enhancement and is robust to spurious minutiae. All the minutiae extracted from our network are subjected to a three stage pose compensation framework: a) view angle estimation based on the location of core point, b) ellipsoid model formulation which simulates and compensate finger pose, c) intersection area estimation and alignment between different view angles. The proposed ellipsoid model is adaptive to both the silhouette of 2D contactless fingerprint image and the estimated view angle. The corresponding area between the different view angles can be theoretically estimated using this model and incorporated to align two contactless fingerprints for achieving superior matching accuracy. Our reproducible experimental results presented in this thesis using public databases, and a database acquired during this work, validate the effectiveness of the proposed framework over the commercial software and earlier methods. Secondly, this thesis presents a minutiae attention network model and the reciprocal distance loss function to enable more accurate contactless to contact-based fingerprint identification. The proposed network contains two branches, a global-net branch to recover global features and a minutiae attention branch that focuses on the local minutiae areas. Attention mechanism is introduced to guide the minutiae attention branch to concentrate on distorted areas and recover minutiae/features correspondence for contactless and contact-based fingerprint images from the same fingers. In addition, reciprocal distance loss is specifically designed to impose strong penalty towards contactless and contact-based fingerprint images from different fingers and guide the network to learn robust features for distinguishing identities. Experimental results on two publicly available databases illustrate significant performance improvements, over state-of-art methods in the literature, and validate the effectiveness of the proposed framework for the contactless to contact-based fingerprint identification. |
| Rights: | All rights reserved |
| Access: | open access |
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