Adapting Convolutional Neural Networks on the Shoeprint Retrieval for Forensic Use

Zhang, Yang; Fu, Huanzhang; Dellandréa, Emmanuel; Chen, Liming

doi:10.1007/978-3-319-69923-3_56

Cited by 14 publications

(13 citation statements)

References 13 publications

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“…Dabei werden in [26] verschiedene Hilfestellungen vorgestellt (Trilogy und Sentinel, Coplik, Forensic Led Intelligence System und Crime Investigation Decision K Support System) und deren Probleme und Grenzen diskutiert. Leider sind Sprachmodelle wie BERT, GPT-3 und Bildmodelle wie Image GPT-3 [27], AlexNet oder VGG16 [28] noch nicht in forensischen Anwendungen integriert.…”

Section: Die Locard'sche Intelligenz: Ki Und Ermittlungsarbeitunclassified

Die Enkel von Locard

Povalej¹,

Rittelmeier²,

Fähndrich

et al. 2021

Informatik Spektrum

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ZusammenfassungDie seit Jahrhunderten verwendeten Methoden in der Forensik basieren auf der Annahme eines Austausches von Materie und Mustern. Durch die Digitalisierung sind diese Annahmen nur noch eingeschränkt gültig und werden hier erweitert und diskutiert. In dem Zusammenhang ist es erforderlich, den Spurenbegriff grundlegend zu überdenken. Gleichzeitig werfen der ständige technische Fortschritt und die immer größer werdende Flut von auszuwertenden Daten die Ermittlungsbehörden immer wieder zurück. Dieser Entwicklung ist nur durch Automatisierung Herr zu werden. Verfahren der Künstlichen Intelligenz können und werden die Ermittlungsbehörden zukünftig dabei zunehmend unterstützen.

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Section: Die Locard'sche Intelligenz: Ki Und Ermittlungsarbeitunclassified

Die Enkel von Locard

Povalej¹,

Rittelmeier²,

Fähndrich

et al. 2021

Informatik Spektrum

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“…Shoeprint images have been studied in many areas, including forensic podiatry [45,86,104], biological traits examination and investigation [62], gender prediction [5,8,63], and body morphological studies [95]. A number of operations underlying shoeprints have been performed including retrieving, recognition, pattern matching via different approaches, and these operations have been performed by many approaches, including manual [3], semi-automated [2,24], automated [1,68], and machine learning (ML) (in particular DL methods) [15,97,104]. Among these, DL methods have shown promising results in shoeprint related operations [14,22,56,104].…”

Section: Literature Studymentioning

confidence: 99%

“…Shoeprints are often found in crime scenes with poor quality images, and they have a critical role in podiatry investigations. Examining shoeprints becomes more challenging with limited available data, poor information contents, unavailability of ground truth and higher quality images, partial and incomplete prints, and most importantly, the lack of domain-specific processing algorithms [2,10,46,69,79,104]. Considering the aforementioned challenges, a higher resolution (HR) shoeprints with reduced noise and enhanced quality have great importance for forensic purposes.…”

Section: Introductionmentioning

confidence: 99%

GUV-Net for high fidelity shoeprint generation

Hassan

Wang

Pang

et al. 2021

Complex Intell. Syst.

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Shoeprints contain valuable information for tracing evidence in forensic scenes, and they need to be generated into cleaned, sharp, and high-fidelity images. Most of the acquired shoeprints are found with low quality and/or in distorted forms. The high-fidelity shoeprint generation is of great significance in forensic science. A wide range of deep learning models has been suggested for super-resolution, being either generalized approaches or application specific. Considering the crucial challenges in shoeprint based processing and lacking specific algorithms, we proposed a deep learning based GUV-Net model for high-fidelity shoeprint generation. GUV-Net imitates learning features from VAE, U-Net, and GAN network models with special treatment of absent ground truth shoeprints. GUV-Net encodes efficient probabilistic distributions in the latent space and decodes variants of samples together with passed key features. GUV-Net forwards the learned samples to a refinement-unit proceeded to the generation of high-fidelity output. The refinement-unit receives low-level features from the decoding module at distinct levels. Furthermore, the refinement process is made more efficient by inverse-encoded in high dimensional space through a parallel inverse encoding network. The objective functions at different levels enable the model to efficiently optimize the parameters by mapping a low quality image to a high-fidelity one by maintaining salient features which are important to forensics. Finally, the performance of the proposed model is evaluated against state-of-the-art super-resolution network models.

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“…In 2017, Richetelli et al [12] postulated that the recent advances in deep learning could carry over to field of footwear classification. Kong et al [13] and Zhang et al [14] were some of the first to apply CNNs to this task. However, they have not considered wear patterns and our work can be seen as a new contribution to the literature on deep learning applications in forensic science.…”

Section: A Shoeprint Classificationmentioning

confidence: 99%

Learning Wear Patterns on Footwear Outsoles Using Convolutional Neural Networks

Francis

Sharifzadeh

Newton

et al. 2019

2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International

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Footwear outsoles acquire characteristics unique to the individual wearing them over time. Forensic scientists largely rely on their skills and knowledge-gained through years of experience-to analyse such characteristics on a shoeprint. In this work, we present a deep learning model that, for the first time, can predict the wear pattern on a unique dataset of shoeprints that captures the life and wear of a pair of shoes. We also present an additional architecture able to reconstruct the outsole back to its original state on a given week, and provide empirical evaluations of the performance of both models.

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Adapting Convolutional Neural Networks on the Shoeprint Retrieval for Forensic Use

Cited by 14 publications

References 13 publications

Die Enkel von Locard

Die Enkel von Locard

GUV-Net for high fidelity shoeprint generation

Learning Wear Patterns on Footwear Outsoles Using Convolutional Neural Networks

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