Deep expectation for estimation of fingerprint orientation fields

Schuch, Patrick; Schulz, Simon-Daniel; Busch, Christoph

doi:10.1109/btas.2017.8272697

Cited by 17 publications

(6 citation statements)

References 13 publications

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“…In addition, the proposed method doesn't need to select the target patterns of characteristic orientation fields. Further, Schuch et al [109] incorporated Deep Expectation into CNN and trained a CNN to estimate FOF called DEX-OF. Compared to Local-izedDic and ConvNetOF, DEX-OF has better performance.…”

Section: Learning-based Methodsmentioning

confidence: 99%

A Survey of the Methods on Fingerprint Orientation Field Estimation

Bian

et al. 2019

IEEE Access

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Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods.

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Section: Learning-based Methodsmentioning

confidence: 99%

A Survey of the Methods on Fingerprint Orientation Field Estimation

Bian

et al. 2019

IEEE Access

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“…[59] introduced a regression convolutional neural network that can estimate the orientation field of an entire fingerprint. A modified version of the same network, based on classification with soft fusion of output labels (deep expectation), was reported to achieve better results in [60]. Other convolutional neural networks for orientation field estimation were described in [61] and [62].…”

Section: Singularities (Deltas)mentioning

confidence: 99%

Exploring the Power of Simplicity: A New State-of-the-Art in Fingerprint Orientation Field Estimation

Cappelli

2024

IEEE Access

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Fingerprint recognition is an important tool for personal identification due to its versatility, user-friendliness, and accuracy. Fingerprint orientation field estimation, a crucial step in fingerprint feature extraction, significantly impacts recognition performance. While numerous methods have been proposed, achieving state-of-the-art accuracy often comes at the cost of increased complexity, hindering practical implementation. This work addresses this challenge by introducing two novel, simple, and efficient fingerprint orientation field estimation methods: GBFOE and SNFOE. Both methods adhere to the KISS (Keep It Simple and Straightforward) principle, achieving remarkable performance on publicly available benchmarks. GBFOE outperforms all local methods and rivals more complex approaches, while SNFOE establishes itself as a new state-of-the-art, achieving the highest accuracy on all datasets in both evaluated benchmarks. Surpassing methods designed specifically for latent fingerprints, SNFOE demonstrates exceptional performance on this challenging task within the evaluated benchmarks, highlighting its generalizability despite not being trained on such data. These results underline the potential of simple and efficient methods in fingerprint orientation field estimation, paving the way for practical and resourceefficient fingerprint recognition systems. An open-source Python implementation of both methods is available, fostering further research and development in this field.INDEX TERMS Fingerprints, Orientation field estimation, KISS principle, FOE benchmarks, NIST SD27.

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“…As an addendum, deep networks have also been used to improve specific sub-modules of fingerprint recognition systems such as segmentation [30], [31], [32], [33], orientation field estimation [34], [35], [36], minutiae extraction [37], [38], [39], and minutiae descriptor extraction [40]. However, these works all still operate within the conventional paradigm of extracting an unordered, variable length set of minutiae for fingerprint matching.…”

Section: Prior Workmentioning

confidence: 99%

Learning a Fixed-Length Fingerprint Representation

Engelsma

Cao²,

Jain

2021

IEEE Trans. Pattern Anal. Mach. Intell.

107

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We present DeepPrint, a deep network, which learns to extract fixed-length fingerprint representations of only 200 bytes. DeepPrint incorporates fingerprint domain knowledge, including alignment and minutiae detection, into the deep network architecture to maximize the discriminative power of its representation. The compact, DeepPrint representation has several advantages over the prevailing variable length minutiae representation which (i) requires computationally expensive graph matching techniques, (ii) is difficult to secure using strong encryption schemes (e.g. homomorphic encryption), and (iii) has low discriminative power in poor quality fingerprints where minutiae extraction is unreliable. We benchmark DeepPrint against two top performing COTS SDKs (Verifinger and Innovatrics) from the NIST and FVC evaluations. Coupled with a re-ranking scheme, the DeepPrint rank-1 search accuracy on the NIST SD4 dataset against a gallery of 1.1 million fingerprints is comparable to the top COTS matcher, but it is significantly faster (DeepPrint: 98.80% in 0.3 seconds vs. COTS A: 98.85% in 27 seconds). To the best of our knowledge, the DeepPrint representation is the most compact and discriminative fixed-length fingerprint representation reported in the academic literature.

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Deep expectation for estimation of fingerprint orientation fields

Cited by 17 publications

References 13 publications

A Survey of the Methods on Fingerprint Orientation Field Estimation

A Survey of the Methods on Fingerprint Orientation Field Estimation

Exploring the Power of Simplicity: A New State-of-the-Art in Fingerprint Orientation Field Estimation

Learning a Fixed-Length Fingerprint Representation

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