ConvNet Regression for Fingerprint Orientations

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

doi:10.1007/978-3-319-59126-1_27

Cited by 12 publications

(3 citation statements)

References 16 publications

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“…• Learning-based -Adaptive-3 [44] (and its improved version), LocalDict [55], ConvNetOF [59], ORI-NET, DEX-OF [60], and its improved version. GBFOE achieves the best performance among local methods on both the "Good" (5.30° RMSD) and "Bad" (14.40° RMSD) datasets.…”

Section: Resultsmentioning

confidence: 99%

“…[58] described a unified fingerprint processing framework implemented by a single network that includes fingerprint segmentation, orientation field estimation, fingerprint enhancement, and minutiae extraction. [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].…”

Section: Singularities (Deltas)mentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

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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“…However, the quality of pattern of target labels is directly determined by the quality of training database, so the reliability of dictionary largely depends on the training database. Schuch et al [108] proposed to train CNNs as a regression to estimate the FOF, namely the ConvNetOF. Compared to Cao et al's classification approach [106], regression is a more natural approach for the estimation of continuous values (The local ridge orientation in fingerprint image is a continuous value).…”

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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OrieNet: A Regression System for Latent Fingerprint Orientation Field Extraction

Liu

et al. 2018

Artificial Neural Networks and Machine Learning – ICANN 2018

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ConvNet Regression for Fingerprint Orientations

Cited by 12 publications

References 16 publications

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

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

A Survey of the Methods on Fingerprint Orientation Field Estimation

OrieNet: A Regression System for Latent Fingerprint Orientation Field Extraction

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